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editor
Dear Readers’
Welcome to Issue 76 of Global Banking & Finance Review.
As financial institutions respond to changing expectations and new demands, this issue highlights how the industry is adapting by prioritising inclusion, trust, and relevance.
Featured on our front cover is Helena Müller, VP Banking Europe at Diebold Nixdorf. "Cash and the Next Generation: Why Gen Z Consumers Still Value Physical Money" (Page 24), Helena Müller of Diebold Nixdorf shares new insight into why cash continues to resonate with younger generations across Europe. From budgeting and privacy to financial literacy and financial inclusion, Gen Z is embracing physical money not as a relic, but as a relevant and trusted choice in an increasingly digital world.
"Banking on Experience in a Rising India: How Standard Chartered is Redefining Wealth for the Affluent" (Page 20), Saurabh Jain of Standard Chartered shares how the bank is combining digital innovation and advisory expertise to support the financial goals of India’s globally minded investors. With platforms like SC Invest and myWealth enhancing accessibility and portfolio transparency, and a growing network of Priority Banking centres, the bank is deepening its reach while retaining a highly personalised approach.
"Asia’s Evolving Scam Defense: Regional Divergence, Rising Prevention, and the Path Toward Collective Security" (Page 6), Anurag Mohapatra of NICE Actimize offers insight into how countries across Asia are strengthening scam response efforts through prevention strategies, sector coordination, and shared data. Drawing on comparative findings from markets such as Thailand, Singapore, and India, he highlights the growing importance of collective intelligence and the need for stronger real-time data sharing.
At Global Banking & Finance Review, we remain committed to bringing you expert insights on the forces shaping the financial sector. Whether you are a leader in banking, fintech, investment, or risk management, we hope this issue provides timely perspective to support your work.
Enjoy the latest edition!
Wanda Rich
Editor
Stay caught up on the latest news and trends taking place by signing up for our free email newsletter, reading us online at http://www.globalbankingandfinance.com/ and download our App for the latest digital magazine for free on Google Play and the Apple App Store
Asia’s Evolving Scam Defense: Regional Divergence, Rising Prevention, and the Path Toward Collective Security`
Anurag Mohapatra, SME and Director of Fraud Strategy, NiCE Actimize
Invisible Payments: From Uber to IoT— What Does a Frictionless Future Look Like?
Navigating the Future of Autonomous Decision-Making
Predicting and Preventing Customer Churn in Retail Banking
Redefining Relationship Banking: Can Human-Centric Service Compete with Hyper-Personalised AI?
Banking on Experience in a Rising India: How Standard Chartered is Redefining Wealth for the Affluent
Saurabh Jain
Managing Director and Head of Wealth Solutions and Affluent Segments in India, Standard Chartered Bank India.
Rethinking SME Credit: Smarter Scoring Models for a More Inclusive Financial System
The Retirement Reckoning: Are We Ready for the 100-Year Life?
Buy Now, Pay Smarter: Is the BNPL Model Finally Growing Up?
The Loyalty Equation: Why Customer Service Is the Ultimate Brand Differentiator
Project Management as a Strategic Advantage: Execution, Efficiency, and Innovation
Marketing’s New Frontier: Customer Segmentation and Personalization in the Data-Driven Age
Cover story
Helena Müller, VP Banking Europe, Diebold Nixdorf
Asia’s Evolving Scam Defense: Regional Divergence, Rising Prevention, and the Path Toward Collective Security`
An interview with Anurag Mohapatra, SME and Director of Fraud Strategy, NiCE
In this interview with Global Banking & Finance Review, Anurag Mohapatra reveals his research on the varying approaches to stopping scams across Asia and compares them to other global approaches. He also shares his vision on how fighting scams will play out in the next three to five years, and notes his belief that shared, collective data is the key to solving these challenges.
What are the most notable insights emerging from your findings regarding Asia’s landscape of scam response strategies?
What strikes me most was the wide variation in approaches across different Asian countries. Some nations—like Thailand, the Philippines, and Hong Kong—are beginning to hold banks, telcos, and digital platforms jointly responsible when scams occur, sharing the burden of accountability. Conversely, countries such as India, Vietnam, and Indonesia tend to place most of the blame— and the financial loss—on the victims themselves.
To better understand this patchwork, in my research I grouped countries into four distinct groups based on two criteria: whether they reimburse scam victims and whether they have strong crosssector coordination involving banks, telecoms, and regulators. Thailand exemplifies the proactive end of the spectrum, having enacted laws that hold multiple sectors liable for scams. On the other extreme, Vietnam and Indonesia focus primarily on blocking scam messages and calls, with limited pathways for victims to recover lost funds.
In between are countries like Singapore and South Korea, which offer limited reimbursement—mostly for phishing scams—and do not extend this support to romance or investment fraud. This diversity illustrates that Asia’s scam response is not a single story but a spectrum where each country is at a different point in its journey.
In your assessment, what trajectory is Asia likely to follow over the next three to four years when it comes to scam responses? Will it adopt a model resembling the UK's approach or that of Australia's?
That is a compelling question. Let us briefly compare the two models: The UK adopts a mandatory reimbursement approach, where banks may be required to compensate victims, sharing liability across both the sender’s and receiver’s institutions. Australia, on the other hand, prioritizes prevention—using measures like confirmation of payee, identity verification, warnings, and payment delays to stop scams before they happen.
Looking ahead, I see Asia adopting a mixed approach. Countries like Thailand and Hong Kong are moving toward the UKstyle model, emphasizing shared responsibility and victim reimbursement. Meanwhile, others such as Singapore and South Korea are leaning toward Australia’s prevention-first strategy, with strong measures to prevent scams but limited redress for victims. Overall, the region is trending toward shared responsibility but not
Actimize
outright mandating reimbursement. Many countries will experiment with voluntary sector agreements or pilot programs before formalizing stricter rules, balancing prevention and redress based on local contexts.
Do you anticipate that regional collaboration and convergence in scam response strategies will increase, or do you expect the landscape to remain fragmented?
I believe fragmentation will persist in the near future—and that is not necessarily a bad thing. Asia is not a monolithic market; it is incredibly diverse—geographically, politically, and culturally. Countries like Singapore, Japan, and Hong Kong have mature institutions and robust consumer protections, while others like Cambodia or Myanmar are still establishing foundational systems.
Some nations—such as Hong Kong and the Philippines—are experiencing real momentum driven by regulators and public pressure to improve their responses. Others are still figuring out the roles that banks, telecoms, and platforms should play.
While we may see some shared tools, like scam reporting portals or fraud intelligence networks, a fully unified regional framework seems unlikely in the near term. Instead, I anticipate “islands of progress”—countries advancing faster and influencing their neighbors—forming a bottom-up pattern of convergence rather than top-down regional harmonization.
From a global perspective, which approach currently demonstrates greater effectiveness: reimbursement mechanisms or preventive controls?
Both have their merits and their effectiveness depends on implementation. The UK model, with its mandatory reimbursement and shared liability, aims to ensure victims are compensated, fostering trust. Australia’s preventionfirst approach attempts to stop scams before they occur, reducing overall losses.Recent data shows that Australia’s scam losses declined from $2.7 billion in 2023 to $2.0 billion in 2024—an encouraging sign that prevention measures are effective. The UK has also seen some success, with losses decreasing slightly and reimbursements reaching as high as 86% in some cases.
However, the most impactful results come from combining both strategies: robust prevention to stop scams upfront and a clear, fair reimbursement process when prevention fails. This integrated approach builds consumer trust and incentivizes institutions to act responsibly, driving better outcomes.
Given that many countries still assign minimal liability to banks and lack protections for authorized scam payments, do you consider this approach sustainable in the long term?
Absolutely not. The techniques used by scammers are evolving rapidly— social engineering, AI voice clones, fake investment apps—and victims often do everything right but still get tricked into authorizing payments.
In most Asian markets, if someone falls victim, the bank typically responds
with “Sorry, you clicked the button,” leaving the victim out of luck. This approach is becoming increasingly unsustainable.
For example, in the U.S., fraud losses hit $10.1 billion in 2024— triple the amount from four years earlier—with a significant share involving authorized payments. Yet, there is no mandated reimbursement, highlighting the need for accountability.
In response, bipartisan bills like the TRAPS Act are emerging to require banks to share liability for scam payments. Without evolving the system to hold institutions accountable, public trust in digital payments risks erosion, and fraud could become an even greater societal problem.
What is one essential aspect of scam response frameworks that remains insufficiently addressed but requires immediate and focused attention?
One area that does not get enough focus is collective intelligence— sharing fraud data across institutions in real time. Currently,
most banks operate in silos, detecting scam indicators or blocking transactions locally, but insights are not shared swiftly enough. Scammers, operating as networks, move quickly from one victim to another.
What we need is a system where fraud intelligence—such as flagged accounts, scam scripts, behavioral patterns—is shared instantly across sectors. Early examples include Australia’s National Anti-Scam Centre, which connects different agencies, and Pay.UK in the UK, working on real-time intelligence sharing.
Advancing this concept is critical because a networked defense is essential to counteract increasingly sophisticated scams. The sooner we can share and act on fraud data collectively, the better our chances of staying ahead of scammers.
Anurag Mohapatra is a recognized expert in fraud prevention and financial crime, with over 18 years of experience working with global financial institutions to combat emerging threats. He currently serves as Director of Product Marketing at NiCE Actimize, where he leads strategic initiatives across the enterprise fraud prevention portfolio. Anurag’s background spans services, solution consulting, and product leadership—giving him a unique 360-degree view of financial crime risk and innovation. He is also the creator and host of AI SPY, a podcast exploring the intersection of artificial intelligence and financial crime.
Anurag Mohapatra, SME and Director of Fraud Strategy, NiCE Actimize
Rethinking SME Credit: Smarter Scoring Models for a More Inclusive Financial System
Small and Medium Enterprises (SMEs) are the engine of global economic development, comprising 90% of businesses and contributing more than 50% of employment worldwide, according to data from the World Bank. Yet, despite their outsized role in job creation and innovation, SMEs remain significantly underserved by traditional financial systems.
Legacy credit evaluation models—built around audited financials, collateral, and established credit histories—frequently exclude early-stage, informal, or rapidly growing SMEs. In doing so, these models misprice risk and restrict capital where it’s most needed. But that is beginning to change.
Advances in artificial intelligence (AI), alternative data sources, and data-sharing frameworks are reshaping how lenders evaluate SME creditworthiness. These tools are enabling a shift from rigid, document-heavy underwriting toward dynamic, inclusive, and context-rich scoring models that reflect the real-time health and potential of a business. This is not theoretical: it’s already being operationalized across Asia, Europe, and Africa through central bank initiatives, fintech platforms, and regulatory pilots.
Rethinking Creditworthiness with Alternative Data
Legacy credit scoring methods often overlook SMEs. A study by the Hong Kong Monetary Authority and ASTRI demonstrated that machine learning models trained on SME transaction and pointof-sale (POS) data achieved AUC scores exceeding 0.91. The models were based on over 74 million monthly observations from 1,000 SMEs, confirming that transactional data can offer strong predictive signals—even in the absence of formal financials.
Supporting this shift is the rollout of the Commercial Data Interchange (CDI), launched by the HKMA in October 2022. The CDI allows for consent-based, standardized data sharing between banks and data providers, covering e-commerce records, tax data, and trade documentation. During its pilot phase, the platform enabled over HK$1.6 billion in SME loans, demonstrating how realtime access to structured data can translate into more inclusive lending outcomes.
These developments point toward a future where creditworthiness is determined not by static financial snapshots, but by a business’s actual performance, behavior, and economic context.
Fintech Innovation and Embedded Scoring Models
Fintech platforms are playing a key role in reimagining how SME credit is assessed. One example is Okredo, a Lithuania-based firm that secured €1.2 million in EU funding to expand its AI-driven credit scoring engine. The platform integrates financial and non-financial data—including tax filings, payment history, and business registry inputs—to produce real-time credit scores for SMEs. Its embedded tools allow lending partners to assess creditworthiness directly through APIs, improving decision speed and enabling continuous monitoring.
The value of platforms like Okredo lies in their ability to evaluate businesses that fall outside the scope of conventional underwriting. By capturing indicators of operational health—such as invoice volume consistency or tax declaration frequency—these systems can score SMEs with limited or no prior credit records. This helps expand access to capital without compromising risk standards.
Beyond speed and coverage, Okredo’s model also demonstrates a shift toward transparency. The company has positioned the platform as a tool that provides tailored risk insights, not just binary approvals—supporting a more collaborative approach to SME lending.
Credit Insights from Digital Behavior
Another major development in SME credit scoring is the growing use of behavioral data—digital footprints generated through everyday business activity. These include mobile wallet usage, online sales patterns, supplier payments, payroll history, and business location stability. Such data, particularly when captured and analyzed in real time, can reveal operational consistency, cash flow rhythm, and business reliability far more dynamically than static financial statements.
Fintech platforms like FundPark are incorporating ecosystem-based data into their lending models. Rather than relying solely on retrospective financials, FundPark leverages inputs from e-commerce platforms, logistics providers, and payment processors to assess trade finance needs. The platform uses what it describes as a proprietary credit model to perform “dynamic data collection and analysis,” enabling real-time credit assessment and monitoring.
The value of behavioral data lies in its ability to provide a forward-looking, real-time picture of SME health. Unlike static credit bureau scores, behavioral signals evolve with the business. This allows lenders to adjust
exposure proactively—scaling up lending as performance improves or flagging early signs of stress. For digitally active enterprises with limited formal credit histories, this approach can offer a path to financial visibility and inclusion that traditional underwriting often cannot provide.
Quantum Machine Learning and Automated Underwriting
As credit scoring models become more complex, some institutions are turning to emerging technologies like quantum computing to increase analytical precision—particularly in low-data or noisydata environments common to SMEs. Quantum Machine Learning (QML) offers the ability to process high-dimensional datasets more efficiently than classical algorithms, uncovering patterns that may be missed by traditional tools.
Pilot studies have explored how QML might support SME lending. For instance, experiments using hybrid quantum–classical models on Singapore-based SME datasets demonstrated that QML models could achieve similar predictive accuracy to classical approaches, but with an order of magnitude fewer training cycles This is especially useful in contexts where robust, structured datasets are lacking.
At the same time, automation is accelerating across the credit lifecycle. By embedding machine learning into underwriting workflows, banks and fintechs can streamline borrower evaluation, reduce operational cost, and speed up approvals. This shift toward modular credit decisioning systems enables real-time adaptation to economic conditions, changing borrower behavior, or new data inputs. The result is a more agile, customizable lending process— one that supports dynamic portfolio management and rapid risk recalibration.
Although quantum applications remain in the early stages, the broader move toward intelligent automation signals a shift from rigid scorecards to adaptive models—making SME financing not only faster, but also more responsive to each business’s actual trajectory.
Governance and Regulatory Frameworks
As credit scoring evolves to include AI, behavioral analytics, and alternative data, financial institutions face growing responsibilities to ensure transparency, fairness, and compliance. These nextgeneration systems are only as trustworthy as the frameworks that govern them.
In Hong Kong, the HKMA has emphasised explainability in AI-powered credit decisioning, mandating that stakeholders— including SMEs—must clearly understand how models arrive
at their risk assessments. Similar expectations are being set out globally, including by the European Banking Authority and the Monetary Authority of Singapore.
Responsible data usage is also a policy priority. As credit models begin to incorporate unstructured data—such as invoice comments, CRM activity, or app-based behavior—it becomes essential that lenders collect information consensually, manage access, and document their use of sensitive inputs.
Trust remains foundational. According to global EY research, 82% of SMEs report they are willing to share additional data if it results in more personalised services. This finding suggests that, with proper safeguards and transparency, SMEs are open to modern data models—creating a tangible opportunity for lenders that operate with integrity and clarity.
Toward a More Inclusive Credit Ecosystem
The convergence of AI, alternative data, embedded infrastructure, and responsible governance is reshaping SME credit scoring into a more dynamic, accessible system. Lenders that embrace this shift are better positioned to serve a segment long underserved by traditional models.
Strategically, data-driven scoring opens new avenues for product design and customer engagement. With deeper insights into operational behavior and financial rhythms, banks and fintechs can offer personalised loan terms, real-time credit line adjustments, and targeted support for business growth. For SMEs, this means faster decisions, fairer terms, and a chance to build creditworthiness through activity—not just documentation.
Financial institutions that invest in transparent, adaptive models also stand to gain a competitive edge in trust. By showing SMEs how data is used, why decisions are made, and how risk evolves over time, lenders can foster long-term relationships grounded in partnership rather than transaction.
However, realizing this potential requires continued collaboration between regulators, technology providers, and financial institutions. Infrastructure like Hong Kong’s Commercial Data Interchange or the EU’s digital finance framework provides the technical and legal scaffolding—but its value depends on widespread adoption and continued innovation.
The opportunity is clear: modern scoring models can bring millions of SMEs into the formal credit system, driving economic resilience and growth. What’s required now is the collective will to implement these tools thoughtfully—and to design credit systems that reflect the businesses they aim to serve.
The Loyalty Equation: Why Customer Service Is the Ultimate Brand Differentiator
Customer service has evolved from a transactional necessity into a strategic cornerstone of brand success. Salesforce’s recent State of the Connected Customer report underscores this shift, revealing that 88% of consumers view the experience a company provides as equally important as its products or services. This rising expectation means businesses can no longer rely solely on product excellence or competitive pricing to secure customer loyalty.
The stakes for companies are high. PwC’s comprehensive analysis of customer experience trends found that 32% of customers will abandon a brand they previously favored after a single negative encounter. Moreover, the same report highlights that repeated poor experiences can lead nearly 60% of customers to permanently sever their relationship with a brand.
For financial institutions, retailers, and service-driven companies, this environment demands a strategic approach: exceptional customer service is now an essential part of brand differentiation, directly influencing customer retention, advocacy, and ultimately, profitability.
Trust Is Built on Consistency
Consistency in customer service is foundational to building consumer trust—and ultimately, brand loyalty. According to McKinsey & Company, delivering consistent service quality across all customer interactions significantly enhances satisfaction, trust, and repeat purchases. Customers increasingly expect uniform excellence across digital, in-person, and customer-support interactions, rewarding businesses that fulfill this expectation with greater loyalty and advocacy.
The financial benefits of consistency are substantial. Research by Bain & Company indicates that companies excelling at consistently positive customer experiences achieve revenue growth between 4% and 8% above their market peers. Conversely, businesses providing fragmented or uneven service frequently face higher churn rates, diminishing customer loyalty, and reduced competitive advantage.
Financial institutions offer clear illustrations of this dynamic. Banks often grapple with inconsistent service due to fragmented internal systems and strict regulatory requirements. Institutions such as
JPMorgan Chase, however, have proactively integrated digital and in-person channels, significantly enhancing their customer experience. This strategic alignment has strengthened client loyalty and solidified JPMorgan’s leadership in an intensely competitive marketplace.
To secure customer confidence amid heightened competition, brands must embed consistency into their operational core. Each customer interaction, regardless of the channel, must reliably uphold the same standards of quality, responsiveness, and care—essential elements for sustained trust and growth.
Emotional Engagement and Human Touch
Creating emotional connections with customers through genuine empathy and personalized interactions is crucial for building lasting brand loyalty. Research by Motista highlights the substantial financial impact of emotional engagement, showing that emotionally connected customers have, on average, a 306% higher lifetime value compared to those who are merely satisfied. This powerful statistic underscores the strategic importance of emotionally resonant service interactions.
Beyond direct financial benefits, emotionally engaged customers are significantly more likely to become vocal brand advocates. Oracle emphasizes in its marketing insights that empathy-driven interactions are essential for fostering deeper trust and building brand resilience, especially during times of disruption or intense market competition. Thus, customer relationships grounded in emotional connections prove more resilient and valuable over time.
Additional insight from CX Network further reinforce the importance of emotional intelligence in customer service, highlighting that representatives trained to respond empathetically and authentically are more successful in creating positive and memorable customer experiences. These humanized interactions transform standard customer encounters into distinctive brand experiences, boosting overall customer satisfaction and long-term loyalty.
To achieve comparable results, organizations should invest proactively in emotional intelligence training for frontline employees and carefully design customer journeys that prioritize empathy and personalized attention. Doing so transforms customer service interactions into authentic connections that not only satisfy immediate customer needs but also drive deeper longterm loyalty and organic advocacy.
Service Recovery and Loyalty
Even the most customer-focused organizations will occasionally fall short. What defines a brand’s long-term relationship with its customers isn’t the absence of failure—but how the brand responds when things go wrong. According to Zendesk, 81% of customers say a positive support experience makes them more likely to return, highlighting the critical role of recovery in shaping loyalty.
This ability to turn a negative interaction into a moment of trustbuilding depends on more than efficiency—it requires empathy, responsiveness, and empowerment. Customers respond best when frontline staff are able to resolve problems quickly and sincerely, without unnecessary escalation. When an issue is addressed in a way that feels human and fair, the result can be a stronger emotional connection than before the failure occurred.
Equally important is how organizations learn from service breakdowns. Recovery isn’t only about the individual experience— it’s a feedback loop. Brands that systematically analyze complaints, identify recurring issues, and adjust internal processes signal to customers that feedback drives real change. Over time, that responsiveness strengthens not only individual relationships, but institutional trust.
Personalization and Data
Personalized service has become a baseline expectation. 71% of consumers expect companies to tailor interactions, and 76% report frustration when this doesn’t happen—especially in moments when their needs are complex, urgent, or repeated. When personalization is executed effectively, it enhances satisfaction and strengthens a customer’s sense of being understood.
Service interactions informed by behavioral context and datadriven tools are key to this shift. Two-thirds of customers now expect brands to understand their unique circumstances, whether through relevant recommendations, seamless transitions between channels, or proactive problem-solving. This expectation is particularly prominent in industries such as banking, insurance, and telecoms, where impersonal service is a known loyalty risk.
The most successful organizations no longer rely on surface-level gestures. Addressing a customer by name is insufficient; today’s benchmarks include anticipating needs, resolving issues before escalation, and adapting to patterns in real time. This level of personalization is increasingly driven by unified customer data platforms, AI-enabled service workflows, and real-time feedback integration.
Beyond improving satisfaction, personalization plays a critical role in reducing churn. When customers are recognized, remembered, and supported across their journey, they’re more likely to return— and more likely to recommend the brand to others. Personalized service isn’t a courtesy—it’s a proven strategy for sustaining longterm loyalty.
Fostering a Customer Centric Culture
Companies that embed the customer perspective into every department—marketing, operations, product, and support—see meaningful gains. Customer centric organizations are 60% more profitable, demonstrating that internal alignment around customer needs delivers real financial value.
This isn’t just a top down mindset; sustainable culture requires operational change. High performing brands provide frontline teams with access to real time customer insights, empower staff to make meaningful decisions, and hold cross functional groups accountable for customer outcomes. When a customer’s needs drive processes and systems, every touchpoint becomes an opportunity to reinforce loyalty.
Leadership commitment is also essential. Executives who regularly highlight customer feedback, reward customer focused behaviour, and model service excellence themselves send a powerful message—customer service isn’t just a department, it’s a company strategy.
In companies where customer centricity is embedded, teams don’t just react to problems—they anticipate and prevent them. This proactive stance shows customers that their concerns matter— and that the brand listens and evolves accordingly. Over time, this position turns individual positive interactions into institutional trust.
The Future of Customer Service and Loyalty
As digital engagement increasingly blurs traditional touchpoints, customers now expect seamless, context-aware support across both physical and online channels. According to an Accenture Interactive study, 91% of consumers say they’re more likely to shop with brands that recognize, remember, and offer relevant recommendations—making personalization across platforms essential in the digital-first era.
While automation and AI enable faster, scalable interactions, they must be balanced with emotional resonance. Industry analysis shows that customers satisfied with digital support are up to 2.7 times more likely to return than those with poor digital experiences, and yet only 64% report satisfaction with digital support compared to 82% who are satisfied with their purchase. This signals that future success will depend not just on speed, but on delivering empathetic, well-designed digital service.
Transparency and responsiveness are also evolving. Customers increasingly expect real-time engagement and clarity via social and messaging platforms. Brands that handle public customer service interactions with empathy and authenticity are far more likely to sustain credibility and loyalty in an era of public scrutiny.
Looking ahead, brands must remain deeply human in their service. Advanced tools should amplify empathy, not replace it—supporting staff, not sidelining them. As service delivery becomes ever more adaptive and emotionally intelligent, real loyalty will hinge not just on outcomes, but on how those outcomes are delivered.
Invisible Payments: From Uber to IoT—What Does a Frictionless Future Look Like?
Invisible payments are financial transactions that happen without any deliberate action at the time of purchase. There’s no card swipe, tap, PIN entry, or confirmation screen. Instead, payment is triggered automatically through stored credentials, sensors, biometric ID, or app-based logic—removing the visible step we traditionally associate with paying.
Uber was one of the first services to make this model feel intuitive. Riders complete a trip and exit the car; the app handles the charge in the background. Amazon Go uses a similar system in retail: customers scan in, select items, and leave. Shelf sensors and cameras track what is taken, and their account is billed automatically, as detailed by Global Payments Integrated
Subscription services like Netflix, Spotify, and HelloFresh also use this format. After an initial opt-in, billing continues without further approval. These invisible flows have become so routine that some analysts describe them as the foundation of modern payment infrastructure, as noted by Prove
More advanced examples are emerging through biometrics and IoT. Alipay’s Smile to Pay system enables facial recognition payments in China, while smart fridges and connected cars are starting to transact autonomously—a direction some fintechs refer to as “ambient finance,” as described by Praxis Tech
These systems make spending feel effortless. But as payment becomes less visible, users may lose track of what they’ve authorized. Reduced visibility can also make it harder to spot errors or dispute a charge as flagged by the Atlanta Fed
Invisible payments are likely to become the default in many industries. But how they’re designed—and how well they balance convenience with clarity— will determine whether they build long-term trust or erode it.
The Evolution of Payment Interfaces
Invisible payments are the result of decades of changes in how people transact. Plastic cards replaced cash, digital wallets streamlined inperson checkout, and embedded systems removed the need for visible confirmation. Each step reduced friction, making the payment process less noticeable. What was once a distinct action has become something that happens quietly in the background.
To understand how invisible payments took hold, it’s helpful to trace the gradual disappearance of traditional payment touchpoints.
From Cash to Card to Contactless
The first significant change in payment behavior occurred with the widespread adoption of magnetic stripe cards, which enabled merchants to process transactions more efficiently than with cash on hand. By the 1980s, debit and credit cards had become the standard payment method across retail environments. Chip-and-PIN authentication replaced signatures in the following decades, introducing PIN verification and encrypted microprocessors to reduce fraud. In the UK, this system became the default by 2006, resulting in a measurable decline in counterfeit card fraud following its rollout.
As payment security advanced, so did speed. Tap-to-pay technology using RFID first gained traction in sectors such as transit and fuel, eventually becoming more prevalent in the retail sector. Card-based payments
overtook cash globally by 2016, and adoption accelerated further during the COVID-19 pandemic as contactless methods became the preferred option for speed and hygiene.
Digital Wallets and Embedded Checkout
Mobile wallets expanded the shift away from physical cards by turning smartphones into secure payment tools. Apple Pay launched in 2014, allowing iPhone 6 users to make in-store and inapp purchases using fingerprint authentication and tokenized card credentials. Within three days of launch, over one million cards were activated, reflecting strong early adoption and public interest in secure mobile payments.
Android Pay followed in 2015, bringing similar functionality to Android users with support for contactless NFC payments and in-app transactions. These platforms helped normalize mobile payments and set new expectations for speed and convenience at checkout.
In e-commerce, Amazon’s one-click checkout patent—granted in 1999—allowed users to bypass traditional checkouts by storing shipping and payment credentials. When the patent expired in 2017, competitors like Shopify and Bolt rapidly adopted the model, embedding instant-purchase functionality into retail flows and further streamlining the online shopping experience.
APIs and Background Payments
Behind these interfaces, the real shift was architectural. Payment APIs allowed businesses to embed checkout directly into websites, apps, and devices, handling tokenization, authentication, and billing in the background. This infrastructure reduced merchant exposure to sensitive data and turned payment into a seamless service layer. API-first providers have enabled these capabilities to scale across verticals, from ride-hailing and food delivery to SaaS billing and mobile marketplaces.
As a result, the payment step increasingly disappeared. Purchases could be completed without redirects, form fields, or even conscious approval, especially for services that operated on subscription or usage-based billing models.
Invisible Payments: The Fourth Wave
A growing number of payments now happen without any visible interaction. There's no tap, no swipe, and no checkout step. Charges are triggered automatically—after a ride ends, when you walk out of a store, or during a subscription renewal. In this model, payment becomes an integral part of the system’s background logic, rather than a distinct action.
Invisible systems rely on tokenized credentials, embedded APIs, contextual triggers, and biometric verification. They eliminate friction—but also raise questions around visibility and control. As these systems scale, financial researchers have warned that consumers may lose track of spending, struggle to identify unauthorized charges, or miss cues that a payment occurred at all—concerns that have been highlighted in regulatory commentary. Each leap in interface design—cash to card, card to mobile, mobile to invisible—has moved payments closer to background
automation. The challenge now is to preserve clarity, trust, and consent in a system designed to be as unobtrusive as possible.
Use Cases Powering the Shift
Invisible payments have become central to how people move, shop, subscribe, and interact with smart devices, eliminating checkout as a visible event across various sectors and geographies.
Mobility & Ridesharing
In ridesharing, invisible payments are already the default. When a ride ends on platforms like Uber or Lyft, the fare is automatically charged to the user’s stored card—no cash exchange, no checkout screen. This payment model helped establish a user experience where booking and payment are decoupled, setting a precedent for embedded finance design in app-based mobility
In Switzerland, commuters using SBB’s EasyRide tap their phone to check in and are automatically billed based on distance when they check out. No physical tickets are issued, and no payment step is presented in the journey—just a notification once the transaction is complete using sensor-based billing
Retail & Smart Checkout
Amazon Go’s “Just Walk Out” system allows customers to enter a store, pick up items, and leave. The moment they exit, the system completes the transaction using computer vision and shelf sensors to determine what was taken and trigger billing invisibly. This cashierless model has since expanded to smaller retail formats, including those in airports, stadiums, and business parks.
In Australia, Canada, and the UK, this approach is being localized across Amazon Fresh and Amazon Go sites. At the same time, Uniqlo has integrated RFID technology into its fitting rooms to enable self-checkout without barcode scanning via tag-based detection.
In the U.S., Starbucks has layered invisible payment into its loyalty app. By 2023, more than 31% of transactions were processed through the mobile platform, where users earn rewards and complete payments without needing to tap or scan at the counter through app-based settlement.
Subscription Economy
Media, delivery, and software platforms now run entirely on invisible billing. Once a payment method is stored, services like Netflix, Spotify, or DoorDash renew automatically. No checkout step is presented; only access or cancellation options are available. While convenient, this model has raised concerns around disengaged billing, where users are charged long after they’ve stopped using a service.
In 2024, research showed that UK consumers lost an estimated £688 million to forgotten subscriptions, with complaints doubling from the previous year. The data prompted regulatory proposals aimed at improving cancellation flows and billing visibility in subscription finance models
IoT-Driven Payments
Smart devices are enabling autonomous commerce. Fridges that reorder groceries when stock is low, cars that pay tolls automatically, and thermostats that adjust energy usage all remove the consumer from the transaction. These payments occur via background logic tied to real-time data, location, or usage thresholds in IoT payment applications
In the UAE, tolling systems charge vehicles automatically as they pass beneath RFID-equipped gates. In the U.S., smart parking systems use license plate recognition to trigger billing as drivers enter or exit garages—no buttons, no confirmation screens. Hotels and stadiums have also adopted invisible flows: Hilton guests can check in, access rooms, and bill in-room purchases to their account without front-desk interaction via fully tokenized guest credentials.
Voice assistants are extending this to conversational commerce. Alexa, Siri, and Google Assistant can complete transactions via stored credentials, with purchases confirmed only after the payment is processed—bringing invisible payments into homes through voice alone.
The Tech Stack Behind the Curtain
Invisible payments depend not just on design—but on a layered technology stack that enables seamless, secure, and real-time transactions. APIs, tokenization, AI, and edge infrastructure all work together behind the scenes to remove friction from the payment experience.
APIs, Tokenization, and Real-Time Payment Rails
At the core of invisible payment flows are payment APIs, which
enable apps, devices, and platforms to connect directly with payment processors without requiring user redirection. These APIs enable transactions to be initiated in the background, whether it's after completing a ride, walking out of a store, or triggering a reorder from a smart fridge— without interrupting the customer journey through frictionless integration.
Tokenization ensures that sensitive card data is never exposed during these invisible flows. Instead, each transaction utilizes a unique, timelimited token that replaces actual account details, minimizing the risk of interception or fraud and enabling background billing for subscriptions, retail purchases, or IoT-triggered payments using token-based architecture.
Powering the movement of funds behind the scenes are real-time payment (RTP) rails, which settle transactions instantly, 24/7. These rails provide the infrastructure for immediate payment confirmation, which is essential for IoT scenarios such as tolling, mobility, or vending, where latency is unacceptable. Modern RTP systems are now API-first and support global peer-to-peer transactions through infrastructure layers, like Thunes
AI and Machine Learning for Fraud Detection and Personalization
Invisible payments remove visible confirmation steps—which means the system must detect fraud without interrupting the experience. AI and machine learning accomplish this by analyzing behavioral data in real-time to distinguish legitimate patterns from suspicious activity. These systems continuously adapt to new threats, flagging anomalies without requiring manual review using real-time risk modeling
Leading processors now use AI to score each transaction in milliseconds. Large networks analyze hundreds of attributes—such as geolocation, device fingerprint, velocity, and network behavior—to instantly identify and block fraudulent attempts, as seen in Mastercard’s deployment
But AI’s role extends beyond security. These systems also personalize payments by automating preferred payment methods, optimizing routing,
and offering contextual recommendations. AI-based payment orchestration enhances UX and efficiency without requiring user input by adapting in real- time.
Cloud Infrastructure, Edge Computing, and 5G for IoT Payments
Invisible payments triggered by IoT devices—like connected cars or smart appliances—require ultra-fast, highly distributed infrastructure. Cloud platforms provide the scale to support millions of background transactions, while edge computing brings real-time processing closer to the device. Processing data closer to where the payment happens cuts down delays. That speed matters when a device is handling something like a toll charge, a vending transaction, or a parking exit. When these systems run on 5G, they can respond almost instantly, even in environments with lots of traffic or movement.
Whether a vehicle is paying for fuel, a vending machine is triggering a product reorder, or a smart city sensor is collecting tolls, this infrastructure ensures payments occur with minimal delay under ultra-low-latency conditions.
As commerce increasingly flows through connected objects and AI agents, the combination of cloud orchestration, local edge intelligence, and 5G communication becomes critical. Invisible
payments will depend on this hybrid environment to operate smoothly, securely, and at scale.
Benefits of Going Invisible
Removing payment steps from customer experience doesn’t just help users—it also improves how businesses run. Transactions move faster, renewals happen without interruption, and fewer errors reach customer service. For subscription services, mobility platforms, and retailers alike, background billing means fewer dropoffs and smoother operations.
Frictionless Customer Experience
Checkout is often where friction slows things down. Even brief delays—reaching for a wallet, confirming a screen, waiting in line—can disrupt the moment. Invisible payments remove these steps. The transaction completes without being the focus, helping customers stay engaged and move through the experience without interruption.
This simplicity matters. On mobile, especially, extra steps can cause people to drop out before finishing a purchase. Research has shown that complex checkout flows are responsible for more
than 70 percent of abandoned carts due to payment friction When checkout disappears, people are more likely to complete the transaction and report greater satisfaction.
Higher Engagement and Loyalty
Invisible payments also support long-term engagement. Subscription and usage-based billing models rely on stored payment credentials to provide uninterrupted access to services. This model reduces churn due to failed renewals or manual input errors and supports predictable, recurring revenue for platforms built on automated billing.
The value goes beyond mechanics. Seamless transactions foster loyalty by removing friction that can otherwise disrupt the customer journey. Research shows that when payments are invisible and personalized, customer lifetime value increases significantly, especially when integrated with loyalty or rewards programs designed around seamless engagement. These systems also collect data on how people pay and what they respond to. That information can help businesses identify when a customer might cancel, or adjust billing and offers based on past behavior by using behavioral signals to improve retention
Operational Efficiency
For businesses, invisible payments streamline operations by reducing manual intervention and overhead. Payment steps are fully automated, lowering demands on POS infrastructure, reducing customer service inquiries, and minimizing input errors. This is particularly beneficial in high-volume sectors like mobility, quickservice retail, and SaaS, where scale demands minimal friction and rapid throughput.
Operational benefits extend across industries. In healthcare, hospitality, and utilities, invisible payments help reduce disputes, speed up billing cycles, and improve collections. These systems also support lower cost-to-serve models, as automation reduces cashier workload and billing errors across channels through embedded, backend processing.
By removing manual steps, businesses can process more payments with fewer delays, errors, or support interventions. These systems scale efficiently, supporting high-volume environments without straining staff or infrastructure, while customers benefit from faster and more consistent service.
Balancing Ease with Oversight
As payments disappear from view, the challenge becomes less about speed and more about control. Without a visible prompt or confirmation step, users may not realize when they’re being charged—or even remember what they’ve subscribed to. These seamless flows, while convenient, can erode a customer’s sense of awareness, especially in services that bill passively or renew automatically.
Design plays a critical role here. Invisible payments demand systems that communicate clearly, not just perform silently. That means surfacing key billing terms, offering real-time alerts, and making cancellations straightforward—so the absence of friction doesn’t result in the absence of consent.
The same applies in connected environments. IoT-triggered payments— from fridges to toll roads—require strong safeguards to ensure users can track what’s been charged, reverse mistakes, and understand who authorized the transaction. In this new model, accountability isn’t removed—it’s relocated into the architecture of the system.
Invisible payments don’t eliminate the need for trust. They raise the bar for it.
Global Readiness: Who’s Leading the Way?
Invisible payments are gaining traction globally—but not uniformly. The maturity, scale, and design of these systems reflect each region’s infrastructure, consumer behavior, and regulatory stance. From the wallet-first super apps of Asia to mobile-powered public transit in the Nordics, and from the privacy-driven frameworks in Europe to infrastructure-leapfrogging in emerging markets, global momentum is clear—even if the paths are distinct.
Asia-Pacific: WeChat Pay, Alipay, and the Super App Economy
China is widely recognized as a global leader in the adoption of invisible payments. Nearly 70% of Chinese consumers use WeChat Pay daily, and Alipay has achieved similar market penetration—together they dominate both in-person and mobile commerce. These platforms allow users to pay via QR codes, facial recognition, or even by walking through sensorequipped stores—no checkout required. What makes them exceptional is their role as multi-service ecosystems: messaging, food delivery, ridehailing, digital identity, and financial services are all integrated into a single app supporting frictionless daily use.
Chinese tourists now expect to use these wallets abroad. WeChat Pay’s overseas transaction volume has grown nearly 240%, driven by crossborder mini-programs embedded into foreign retail, ride-hailing, and transit systems, demonstrating global wallet portability.
Nordic Countries: Mobile Pay as the Default
The Nordic region—comprising Finland, Sweden, Norway, Denmark, and Estonia—has quietly become a global showcase for invisible payments. In Sweden and Norway, only around 10% of transactions are still made in cash, and mobile apps like Swish, Vipps, and Mobile Pay have become the default for peer-to-peer and retail payments, used by more than 75% of residents.
Mass transit systems in Helsinki, Copenhagen, and Oslo now allow passengers to board buses and trains using contactless mobile payments—with no advance ticket or paper trail. These systems are increasingly offline-capable, offering resilience during service disruptions
or in low-connectivity zones through integrated offline fallback infrastructure
Despite high-tech adoption, some Nordic governments have introduced laws requiring merchants to accept cash, ensuring that society remains resilient to digital outages or inclusion gaps as part of national contingency plans
Invisible Payments in the U.S. and EU: Two Diverging Paths
The U.S. and the European Union represent two distinct philosophies regarding the rollout of invisible payments.
In the U.S., the focus is on speed and seamlessness. Invisible billing is embedded in ridesharing (Uber), retail apps (Starbucks), and subscription models (Amazon, Netflix). The regulatory environment emphasizes market-led innovation, giving platforms freedom to deploy background billing, app-based payment triggers, and loyalty integration with limited friction.
In contrast, the EU imposes stricter protections around data privacy and user consent. Platforms must obtain explicit optins, clearly communicate billing terms, and offer straightforward opt-out mechanisms. These requirements stem from legal frameworks that treat payment behavior and billing data as personal information—subject to full user control and protection under consent-first design principles
This divergence shapes global product design. U.S. systems prioritize invisible speed; EU systems prioritize visible accountability. Developers must build with entirely different assumptions about what users must see—and when they must approve it.
Emerging Markets: Leapfrogging the Visible Phase
In many emerging economies, invisible payments are not a luxury, they are the payment infrastructure. In India, the Unified Payments Interface (UPI) handled over 130 billion transactions in FY2023, supporting 24/7 instant payments via QR code, app triggers, and API-based merchant flows. UPI enables even informal workers to receive payments instantly, using nothing but a mobile number or QR code, serving both banked and unbanked populations.
In Brazil, Pix has revolutionized real-time settlement. Since its launch in 2020, it now processes over six billion transactions monthly, used for everything from government transfers to embedded e-commerce purchases. The upcoming “Pix Automático” feature will allow background recurring billing— enabling utility subscriptions and invisible charges in a market where many consumers lack credit cards via national real-time infrastructure.
In Africa, platforms like M Pesa allow users in Kenya and beyond to pay bills, transfer money, and even trigger microloans using mobile phones—with no need for cards or bank accounts. These systems have evolved into invisible payment layers powered by SMS triggers and device-embedded wallets supporting financial inclusion at scale.
Emerging markets are not just catching up—they are pioneering invisible payment models that reflect local constraints and leapfrog legacy infrastructure entirely.
What a Frictionless Future Might Look Like
Invisible payments are evolving from convenience features into a foundational layer of smart infrastructure. In tomorrow’s cities, you won’t just make payments; they’ll happen for you, automatically, as part of how digital services work.
In multi-modal transit systems, users will move between bikes, buses, tollways, and trains without opening an app or scanning a code. Instead, contactless sensors, biometrics, or contextual triggers will link the journey into a single background transaction— transforming transportation into an ambient financial flow built into urban infrastructure through seamless mobility integration
As embedded finance merges with ambient computing, financial services will vanish into the tools and environments we already use. Banking payments, lending, insurance—will embed within rideshare apps, e-commerce platforms, and SaaS interfaces. Users will not interact with their bank; instead, the service will act on their behalf, invisibly facilitating everyday tasks like paying utility bills based on real-time usage or initiating checkout before a customer reaches the counter as part of adaptive, intelligent commerce
But the frictionless future requires more than speed—it needs trust. As systems automate increasingly complex decisions, consumers must believe their data is safe, their charges are accurate, and their ability to intervene is preserved. Trust becomes the currency of invisible payments: the foundation that allows automation to scale responsibly, as emphasized in infrastructure-grade design models
Financial institutions that serve as the back end for these flows— handling regulatory compliance, identity verification, and audits— will remain invisible to users but essential for system integrity. Invisible doesn’t mean unregulated; it means users don’t see the complexity, but benefit from its presence within embedded finance frameworks
The future of invisible payments will be defined not just by what users don’t do—but by how securely and intuitively their systems act on their behalf.
The Delicate Balance
Invisible payments offer a compelling vision: effortless transactions, embedded commerce, and real-time financial interaction that feels natural, seamless, and even invisible. But delivering this vision requires vigilance.
The very strengths of invisible systems—automation, convenience, and abstraction—can quickly become risks if not paired with consent, visibility, and accountability. As spending becomes passive, users risk losing track of how money moves. Systems that remove friction must also preserve control.
Designing for the future means anchoring innovation in ethics: giving users clear ways to understand, authorize, and dispute transactions without needing to reintroduce friction. In this next chapter of commerce, the challenge is to build systems that are invisible only in execution—not in oversight.
Can we create a world where payments occur without prompting, yet always with our consent? The success of invisible payments will depend not on how little users interact—but on how they can intervene when needed.
Banking on Experience in a Rising India: How Standard Chartered is Redefining Wealth for the Affluent
India’s affluent segment is expanding—driven by entrepreneurial growth, rising global mobility, and a growing appetite for more sophisticated financial solutions. With a presence in India dating back over 160 years, Standard Chartered Bank is building on that legacy to deepen its role as a trusted partner in wealth. Saurabh Jain, Managing Director and Head of Wealth Solutions and Affluent Segments in India, offers insight into how the bank is strengthening its digital platforms, elevating advisory capabilities, and building tailored offerings for clients who increasingly expect both global access and local expertise.
He began by describing the current position held in the market by Standard Chartered, which has been present in India for over 160 years. “Standard Chartered is among the oldest foreign banks in India, and has a wide footprint of 100 branches spread over 42 cities,” he said. “In addition to a full-service universal bank, the group has a Non-Banking Finance lending entity (Standard Chartered Capital) to complement the bank’s presence in specific geographies and segments and a Retail securities broking business (Standard Chartered Securities India), and is the first foreign bank to have commenced banking activities in India’s International Financial Services Centre (in GIFT City, Gujarat).”
Home to a sizeable and profitable Standard Chartered franchise, India has been among the top contributors to its global business over the past few years. “We are well-positioned to participate in the India opportunity, be it in the opening of new supply chains, increased manufacturing, or the country’s focus towards infrastructure building and sustainable finance.
“Our retail franchise, or the WRB (Wealth and Retail Banking) segment as it is called, serves the needs of affluent clients in India,” Saurabh continued. “Our affluent offering is underlined by a full-service wealth proposition catering to the needs of clients across investments, insurance, forex, equity broking, lending, and the capability to manage discretionary and advisory portfolio mandates. This is complemented by a strong digital backbone, with straight-through transaction capabilities across all key wealth products and industry-leading investment platforms like SC Invest and myWealth.”
As one of the largest foreign banks in India, both in terms of its distribution presence and assets under management, Standard Chartered’s wealth business in India has seen strong double-digit growth for the past 4 years, and India is among the top 5 markets for the Standard Chartered Group on wealth. “We are doubling down on our affluent and wealth business in India by significantly increasing our RM (Relationship Manager) capacity and investing further in technology innovations. We have launched state-of-theart Priority Banking centres at 14 locations in India and will expand to a further 10 by the end of 2025, reflecting our commitment to elevating client experience and engagement for our affluent clients.”
He went on to acknowledge how India’s growing affluent and emerging middle-class populations are becoming increasingly international in their outlook, in terms of growing both their business and their wealth, and how Standard Chartered is
responding to these changing financial needs. “With a presence across key markets in Asia, Africa and Jersey, our global network allows global Indians to access seamless cross-border solutions across wealth and lending. We are also evolving our product suite to address the evolving requirements of affluent clients, with bespoke solutions across alternate assets and private markets, and building advisory capabilities across asset classes.
“We approach the emerging affluent client as a segment that has a high propensity and demographic profile to become future affluent clients,” he continued. “They are usually at a life stage where they need a broader range of solutions across savings, lending, insurance and wealth. Our offerings are designed to address these key needs, with technology and digital serving as a key backbone. We continue to innovate using digital and data to ensure client experiences become smoother, faster and more personalised.”
Platforms like myWealth and SC Invest continue to enhance the bank’s wealth management offering, and Saurabh shed some light on the impact they have had on client engagement and access to investment services. “At its core, Standard Chartered strives to be a client-centric, data-driven, digital bank,” he explained. “This is underpinned by a commitment to establishing strong foundations in technology, information and cybersecurity, reframing technological transformation, and driving process excellence.”
Housed under the SC Mobile App and Online Banking, SC Invest is a stateof-the-art digital platform that allows clients to get onboarded, perform risk profiling, transact in mutual funds, view portfolio holdings across funds, savings accounts, and equities, and undertake reviews. As Saurabh described it, “SC Invest is a one-stop, online investment shop allowing clients to also read market insights, listen to our podcasts, undertake detailed fund comparison, and execute transactions through RM-assisted journeys.
“The SC Invest platform forms an integral part of our Wealth Management Strategy. It revolutionises the customer experience by offering seamless digital onboarding, efficient transaction execution and comprehensive portfolio review, and empowers clients with unprecedented convenience and control over Systematic Investment Plans, where the customer can Pause, Cancel and Resume SIPs at their fingertips.”
In addition, myWealth is an RM-facing platform that allows Relationship Managers to seamlessly perform portfolio reviews, generate actionable insights on portfolio health, and generate model portfolios aligned with Standard Chartered’s house views. “myWealth, equipped with in-house advanced analytics, empowers Relationship Managers and Wealth Specialists to better engage clients digitally with holistic and comprehensive portfolio reviews and personalised investment insights, and generate fully customisable portfolios tailored to clients’ goals and preferences,” Saurabh said. “It combines the bank’s in-house advanced analytics and investment expertise to generate personalised investment ideas with three key elements in the view: the client’s profile and risk tolerance, current investments, and alignment with our CIO house views.
“With myWealth, all necessary information for a portfolio review is in-built and housed in the tool, which is readily accessible from an RM’s iPad. RMs are now able to complete a portfolio review with a client on a single platform in just 30 minutes. This reduces the portfolio review time and improves the client experience significantly.”
Saurabh Jain
Managing Director and Head of Wealth
Solutions and Affluent Segments in India, Standard Chartered Bank India.
Since digital innovation is often the most impactful when it complements human expertise, Standard Chartered’s digital platforms and advisory teams are designed to work in harmony to provide a seamless, personalised client experience, as Saurabh explained. “The digital platforms leverage advanced analytics on user behaviour and preferences to provide actionable insights,” he said. “Our advisory teams use these insights to tailor their solutions to each client's unique circumstances, ensuring that the human touch is always present in client interactions.
“Our digital platforms also ensure that clients can access personalised investment dashboards and track their portfolios online. These tools provide real-time updates and insights, allowing clients to stay informed and engaged with their financial plans. Advisors are available on-call, through tools like myRM, to discuss these insights, answer questions, and provide expert guidance. This integration ensures that clients receive personalised insights that are both informed by data and enriched by human expertise.”
This approach relies on seamless communication channels, integrating omni-channel access and advisor accessibility. “Clients can interact with us through multiple channels, including mobile apps, web portals, phone, and in-person meetings,” Saurabh revealed. “Our digital platforms ensure that all these channels are
integrated, providing a consistent and seamless experience. Advisors can access the same integrated information, allowing them to provide informed advice regardless of the communication channel the client prefers.”
He also highlighted how client engagement is enhanced with the use of proactive alerts and notifications with advisory follow-up. “Our digital platforms can send proactive alerts and notifications about market changes, portfolio performance, and other relevant updates. This keeps clients informed and engaged. Advisors can follow up on these alerts with personalised advice and recommendations, ensuring that clients can take timely and informed actions.”
A major advantage of serving clients in this way is that it allows for a continuous improvement and feedback loop through client feedback, innovation and adaptation. “We continuously gather feedback from clients through our digital platforms and advisory interactions,” Saurabh said. “This feedback is used to improve our services and tailor our offerings to better meet client needs. Our teams work together to innovate and adapt our digital tools and advisory services based on client feedback and emerging trends, ensuring that we stay ahead in providing exceptional client experiences.
“By integrating cutting-edge digital tools with the expertise of our advisory teams, we ensure that clients receive a holistic, personalised,
and seamless experience that leverages the best of both worlds.”
Standard Chartered’s multiple business lines and client segments are no obstacle to cross-functional collaboration, as Saurabh affirmed that at the organisation’s core is a one-bank approach. “Strategic collaboration across Corporate and Retail Banking is a key focus area for the bank. For example, through strategic partnerships with top corporates, we provide a one-stop service to meet both corporate and employee needs, leveraging our Employee Banking solutions.
“India’s economic growth is being driven by a rapidly growing pool of entrepreneurial and tech-centric creators. By leveraging our strong presence in the SME segment, we are extending wealth management services to the owners and promoters of these businesses to address their financial needs and aspirations.
“Our international network also means that the opportunity to drive cross-border synergies and address the global banking and wealth requirements of affluent clients is significant,” he added. “We are able to tap into our corridor presence across markets and ensure clients get the best investment opportunities across those markets.”
When shaping new product offerings or expanding into new areas, the key for Standard Chartered has been to centre the needs of affluent clients and how they have evolved over the past decade, which has seen increasing demand for sophisticated products and solutions that meet their investment objectives and capacity for risk. “With significant entrepreneurial wealth creation happening in the past few years, wealth management in India has kept pace by coming up with innovative solutions and structures customised to the needs of clients,” Saurabh reported. “The affluent Indian increasingly has a global outlook, both in terms of business expansion and wealth management.”
He confirmed that Standard Chartered’s offerings have developed in step with these requirements. “As a full-service wealth solutions provider, we have offerings across investments, insurance, foreign exchange, lending, securities operations, and the capability to manage advisory and discretionary portfolio mandates. Over the past two years, we have significantly expanded our alternate product offerings, enabling access to private markets, exclusive strategies with reputed managers and expanding advisory across asset classes.
“As an international wealth manager, we help clients access cross-border investment opportunities. We have a strong corridor presence in markets such as Singapore, UAE, Hong Kong and Jersey, helping global Indians access investment opportunities both in India and their country of residence. We are also leveraging our presence in GIFT City to offer curated solutions for global Indians.”
He concluded with a look ahead to what the future holds for Standard Chartered Bank India. ”Product innovation is a continuous journey for us at Standard Chartered. Over the course of this year, we will be launching enhanced advisory tools for UHNW clients, along with expanding our alternate and private market offerings,” he revealed. “We are also expanding our platform capabilities, adding digital onboarding journeys across wealth products, enhancing our portfolio reporting platforms and capabilities, and augmenting functionalities in existing platforms like SC Invest and myWealth.”
Finally, in terms of the key strategic initiatives shaping the future of the bank, he offered some insight into what clients can expect next. “We are pursuing a conscious pivot to bank the affluent clients in India, expanding our coverage and presence in the top cities of India, while also enhancing our products and value proposition to cater to the affluent segment. These changes, which will roll out over the next few months, are aimed at making Standard Chartered a banking partner of choice for the affluent Indian, addressing their international banking and wealth management needs seamlessly.”
Cash and the Next Generation:
Why Gen Z Consumers Still Value Physical Money
by Helena Müller, VP Banking Europe, Diebold Nixdorf
Have you ever heard of the Lindy effect? This theory suggests that the longer something has been around, the longer its remaining life expectancy. In other words, if a technology or idea has survived for decades, or even centuries, it is more likely to persist well into the future. Cash is a perfect example of this phenomenon. Introduced over 3,000 years ago1, physical currency remains not only relevant but surprisingly essential in our increasingly digital world. And contrary to the popular narrative that digital natives are abandoning cash, younger generations in Europe are showing that the future of payments is a blend of physical and digital solutions, where freedom of choice is paramount.
Access to cash is still a deal-breaker for most Europeans, regardless of their demographic. A recent YouGov study commissioned by Diebold Nixdorf found that between 70 and 90% of consumers would not open an account with a bank that doesn’t provide easy access to cash withdrawals. This challenges the assumption that modern banking can be exclusively digital. Despite the soaring popularity of contactless payments, mobile wallets, and instant transfers, the ability to withdraw physical money remains a core banking expectation, even among young people.
While cash is stereotypically regarded as just a habit of older people, consumer research shows that Gen Z individuals are increasingly embracing physical money. In Ireland, for example, young adults aged 18-24 are the heaviest users of cash, with 35% reporting that they use it daily2
This affinity for cash can be explained by several factors, including security concerns and budgeting needs.
Tech-savvy and digitally native, Gen Zers and Millennials are all too familiar with data breaches and cybersecurity threats. They worry about data privacy in electronic payments and view cash as a safer alternative. Unsurprisingly, anonymity was cited as the biggest advantage of cash by younger Germans (aged 18-39) who participated in a recent YouGov survey commissioned by Diebold Nixdorf.
Cash is also well known as an antidote to overspending. Its physical nature helps visualize limits and build greater financial awareness. In recent years, a budgeting trend known as “cash stuffing” has gained popularity on social media, with Gen Z influencers demonstrating how to allocate money to envelopes for specific purposes, such as holidays, eating out, or car-related expenses.
This hands-on, visual approach to money management isn’t limited to adults. For generations, parents and educators have relied on physical money to teach children the fundamentals of spending, saving and budgeting. This trend continues today with Generation Alpha, comprising children born between the
early 2010s and the mid-2020s. Using coins and banknotes helps the youngest members of society connect with the value of money in a way that digital transactions can’t replicate. In fact, 73% of Swiss parents consider cash to be the most suitable means of payment for children to develop financial literacy and learn how to handle money, because it is tangible, valuable, and limited3
Cash is also the most accessible and inclusive payment method because it doesn’t require smartphones, internet access or digital literacy. This matters to young people, who tend to advocate for greater social equity. Research conducted by NielsenIQ on behalf of Diebold Nixdorf indicates that banks' diversity and inclusion efforts, as well as their commitment to environmental, social, and governance principles, translate into revenue advantages and higher customer loyalty, particularly among Millennials and Gen Zers.
The revival of cash among younger generations also carries an element of nostalgia. The majority of Millennials and Gen Zers report feeling digitally fatigued4 and turn to analog hobbies such as knitting and film photography to take a break from their screens. Similarly, the tactile experience of cash offers a welcome respite from tech overload.
Younger Europeans are not rejecting electronic payment methods; rather, they are seeking freedom of choice, and cash continues to play an active role in this. Ultimately, consumer satisfaction isn’t about choosing between cash and digital payments; it's about having both options available. The best customer experience offers flexibility, allowing people to choose the right payment method for each situation.
1
Helena Müller, VP Banking Europe, Diebold Nixdorf
Navigating the Future of Autonomous Decision-Making
The idea of machines making decisions on our behalf once belonged to the realm of science fiction. Today, it is embedded in the everyday—guiding what news we see, what jobs we’re offered, and in some cases, determining access to financial services, healthcare, and justice. From AI-powered hiring platforms to autonomous vehicles and algorithmic loan underwriting, artificial intelligence (AI) is rapidly transforming the decision-making landscape.
According to McKinsey, nearly three-quarters of organizations have now deployed AI in at least one function—up from just over 50% two years ago, underscoring how deeply embedded these systems have become. Yet as machines assume more authority, the stakes grow higher. At the core of this shift lies a fundamental question: how can we ensure these systems reflect our ethical values, remain accountable, and deliver outcomes that are fair, transparent, and just?
What’s at Stake When Machines Decide?
AI systems are increasingly capable of tasks once demanding human expertise. A study in npj Digital Medicine shows AI can match or outperform clinicians in certain diagnostic roles— especially in radiology and dermatology.
This acceleration has spurred hopes of faster, more consistent, and less biased decision-making. However, it also introduces ethical tensions when AI directly impacts lives. MIT’s Moral Machine project revealed significant regional differences in how people believe autonomous vehicles should make life-and-death decisions, highlighting the cultural and societal dimensions of machine ethics. As MIT News reported, these findings raised critical questions about how AVs should be programmed to reflect the diverse moral expectations of global populations.
Take autonomous vehicles, for instance: a real-world (and unavoidable) version of the “trolley problem”, a classic ethical dilemma that asks whether it is more justifiable to take an action that sacrifices one person in order to save many. Engineers and regulators are now tasked with programming similarly high-stakes decisions into machines. Should a self-driving car prioritize the safety of its passengers, or the pedestrians in its path? Should it minimize overall harm, even if that means actively choosing who is harmed? While many systems are designed to reduce risk probabilistically, these questions remain deeply unresolved. There is no universal framework for ethical decision-making in autonomous vehicles, leaving developers to navigate murky moral terrain with lasting societal consequences.
Bias and Accountability
One of the most persistent challenges in AI development is algorithmic bias. Because machine learning models are trained on historical data, they often replicate the societal inequalities embedded in those datasets. Without careful design, these systems don’t just reflect human bias, they can amplify it.
In healthcare, a widely used risk prediction algorithm assigned lower health risk scores to Black patients than to white patients with the same medical needs. This was because the model used healthcare spending as a proxy for illness, which failed to account for systemic disparities in access and treatment. As a result, fewer Black patients were referred for advanced care despite having similar health conditions.
In the realm of facial recognition, a landmark study by the U.S. National Institute of Standards and Technology (NIST) found that commercial facial recognition systems misidentified Asian and Black faces at far higher rates than white faces, raising red flags about their use in law enforcement, surveillance, and public safety.
Bias in hiring algorithms has also been well documented. Amazon abandoned a machine learning hiring tool after it began favoring male candidates and penalizing resumes that mentioned terms like “women’s chess club” or all-women’s colleges. The system had been trained on resumes submitted over a ten-year period, most of which came from men, embedding historical bias into automated selection.
Adding to these technical concerns is automation bias, the cognitive tendency to place undue trust in machine-generated outputs. Studies have shown that individuals are more likely to accept recommendations from AI systems even when those recommendations contradict better judgment or established procedures. This behavior has been observed in clinical environments, where practitioners may follow flawed algorithmic guidance, as well as in workplace tasks, where overreliance on flawed outputs can lead to serious consequences. As reliance on AI expands, the risk is not only that systems produce biased outcomes, but that human users may be less likely to question them.
These issues raise critical questions about responsibility. When an AI system causes harm—by issuing a discriminatory loan denial, misidentifying a suspect, or delaying medical care—who is held accountable? Is it the software developer, the company deploying the system, the data provider, or the algorithm itself? Current legal systems often fall short in assigning clear liability.
The European Union’s proposed AI Act is one of the first comprehensive attempts to address this. It classifies AI systems according to risk and
places legal obligations on providers and users of high-risk systems, such as those involved in credit scoring, employment, or public services. But in most jurisdictions, clear accountability standards are still lacking—leaving individuals exposed to algorithmic harm without clear avenues for recourse.
Transparency and Explainability
As AI models become more sophisticated, many operate as “black boxes”—their decision-making processes are opaque even to experts. This poses a significant concern in domains like healthcare, finance, and justice, where understanding why a system reaches a decision is critical. A survey of XAI techniques underscores the complexity of interpreting deep-learning models and emphasizes the need for greater transparency in high-stakes settings.
To address this, the field of Explainable AI (XAI) has developed techniques that unveil the logic behind black-box decisions— examples include feature attribution methods, sensitivity analyses, and post-hoc model visualizations.
The Alan Turing Institute, in partnership with the UK Information Commissioner’s Office, released “Explaining decisions made
with AI”: guidance and practical workbooks that offer a structured governance framework and tools for implementing explainability across public sector AI systems. Their AI Explainability in Practice workbook outlines clear criteria for determining when and how to provide human-readable explanations for AI-supported outcomes.
These efforts—anchored in peer-reviewed research and policydriven frameworks—illustrate a growing consensus: transparency in AI is both a technical challenge and an ethical imperative.
Charting a Path Forward
As AI becomes deeply embedded in mission-critical sectors, governance must evolve from advisory frameworks to enforceable standards. The European Commission’s Ethics Guidelines for Trustworthy AI, developed in 2019 by its High-Level Expert Group on Artificial Intelligence, established seven guiding principles that AI systems should uphold—ranging from human agency and technical robustness to transparency and societal well-being. These guidelines were not only aspirational but designed to influence both policy and product development across the European Union.
To support practical implementation, the Commission introduced the Assessment List for Trustworthy Artificial Intelligence (ALTAI),
an interactive tool that enables developers and institutions to assess their systems against the ethical criteria outlined in the guidelines. ALTAI promotes accountability by encouraging AI practitioners to reflect on real-world risks and document mitigation strategies throughout the design and deployment process.
Building on this foundation, the proposed EU AI Act marks a pivotal shift from voluntary adherence to legal enforcement. It introduces a tiered, risk-based regulatory framework that classifies AI systems into categories such as minimal risk, limited risk, high risk, and unacceptable risk. High-risk applications—such as those used in biometric identification, critical infrastructure, credit scoring, or employment—will be subject to strict requirements, including transparency disclosures, human oversight mechanisms, and postmarket monitoring obligations. This legislation aims not only to protect fundamental rights but also to foster innovation by providing legal clarity and harmonization across the EU.
In the United States, regulatory agencies have so far prioritized enforcing existing consumer protection laws over creating AI-specific legislation. A pivotal example is the Consumer Financial Protection Bureau’s Circular 2022-03, which reaffirmed that lenders must comply with the Equal Credit Opportunity Act by providing specific, individualized reasons for denying credit—even when decisions are made using complex or opaque AI models. The guidance makes clear that algorithmic decision-making does not exempt financial institutions from established legal obligations.
This interpretation was reinforced in Circular 2023-03, which clarified that lenders cannot rely on generic checklists or sample disclosures when issuing credit denial notices. Instead, they must ensure that explanations accurately reflect the actual factors that influenced each decision, even if those factors originate from a machine learning model. The guidelines for institutions using algorithmic
systems make clear that there is no exemption from transparency obligations, underscoring that fairness and explainability remain legal requirements, regardless of the technology involved.
Complementing regulatory oversight, ethical AI deployment also depends on human-in-the-loop systems, independent algorithm audits, and participatory design. Civil society advocates such as the Algorithmic Justice League continue to emphasize that inclusive development teams and meaningful community engagement are essential to detecting and correcting bias before systems are deployed.
Together, these evolving approaches—from enforceable EU frameworks to U.S. legal reinforcement and grassroots accountability—signal a broader shift: from aspirational ethics to structural safeguards. As AI becomes more integral to decisionmaking processes, building and maintaining public trust will depend on how effectively these systems are governed.
The Human Element
Although AI systems are capable of simulating intelligence, they remain tools shaped by human choices—both explicit and implicit. Whether in the data used to train them, the metrics optimized during deployment, or the policies governing their use, the ethical foundation of AI is built by people.
The central challenge is not whether machines can be moral actors. It is whether we, the humans behind them, are willing to accept responsibility for their actions and outcomes. That requires transparency, intentional design, and proactive governance.
The era of autonomous decision-making is already here. The real question is whether we are prepared to direct its trajectory in a way that supports fairness, accountability, and the public good.
Project Management as a Strategic Advantage: Execution, Efficiency, and Innovation
Project management has become a defining capability for organizations delivering complex strategic initiatives. Across industries—from banking and infrastructure to technology and manufacturing—success now depends not only on innovation or capital, but on the ability to execute projects reliably, at scale, and under growing scrutiny. Yet, the gap between strategy and delivery remains wide, with costly delays, missed milestones, and budget overruns undermining performance.
Findings from the Project Management Institute indicate that approximately 11.4% of organizational investment is lost due to poor project performance. While this represents an improvement over previous years, the scale of waste remains substantial, driven by recurring issues such as unclear objectives, planning deficiencies, resource constraints, and governance gaps. Organizations with strong project cultures are better positioned to address these risks and protect long-term value.
From Coordination to Strategy: Repositioning the Project Function
The role of the project management office (PMO) is undergoing a fundamental shift. No longer confined to coordination and compliance, the PMO is being repositioned as a strategic partner responsible for aligning project execution with broader business goals.
This change is being shaped by the rising complexity of enterprise initiatives. As organizations invest heavily in digitization, regulatory compliance, and ESG alignment, senior leaders require greater transparency into how these efforts are prioritized, resourced, and governed.
In response, many companies are elevating their PMOs into enterprise-level entities that influence investment decisions and help maintain strategic alignment. Rather than enforcing standardized templates or tracking timelines alone, strategic PMOs now act as portfolio management hubs. They support crossfunctional planning, identify value drivers, and assess delivery risk in real time.
Their focus is not simply on whether a project finishes on time, but whether it contributes to measurable business outcomes—be it efficiency gains, regulatory clearance, or competitive advantage. In many organizations, PMOs now report into transformation offices or directly to senior leadership, enhancing their ability to guide prioritization and execution.
This repositioning also demands new capabilities, including fluency in delivery frameworks, financial planning, risk management, and stakeholder engagement.
In short, the PMO is no longer a coordinating function—it is a control tower for execution. Its effectiveness can determine whether strategic initiatives move forward with discipline and impact, or stall due to fragmentation and drift.
Cross-Industry Contrasts: Understanding the Execution Gap
While project failure is a global concern, the underlying causes often differ by industry. In financial services, execution challenges frequently stem from regulatory complexity, siloed legacy systems, and evolving compliance demands. Projects involving core system replacements, payment modernization, or data governance can be particularly vulnerable when business objectives and technical execution are not fully aligned.
In the technology sector, where innovation cycles are fast and iterative, projects face different pressures. Here, the risk is less about cost overruns and more about market timing, product relevance, and stakeholder alignment. Ambitious product development efforts can stall or be deprioritized when regulatory conditions shift, strategic goals evolve, or user feedback diverges from early assumptions.
Infrastructure and construction projects present yet another profile of risk. These initiatives typically span multiple years, involve large budgets, and depend on multi-party coordination. Challenges such as permitting delays, procurement disputes, supply chain disruptions, or scope inflation can derail even well-planned programs. In these environments, effective scheduling, risk tracking, and contingency planning are critical to maintaining control.
Despite their differences, these sectors share a common theme: when governance is fragmented or planning is insufficient, the likelihood of failure increases. An effective project management— tailored to the context and complexity of each industry—can help close the gap between intention and execution.
Choosing the Right Delivery Model
Choosing the right delivery model is a foundational decision that shapes how projects are structured, managed, and executed. In this context, the delivery model refers to the methodological framework guiding execution—most commonly Agile, Waterfall, or a Hybrid suited to project scope, regulatory exposure, and stakeholder needs.
Agile delivery emphasises iterative development, adaptive planning, and continuous stakeholder involvement. The Standish Group’s CHAOS Report shows that Agile projects are three times more likely to succeed than traditional Waterfall projects—particularly in contexts that demand flexibility and frequent feedback cycles.
Waterfall delivery, by contrast, remains effective in environments with stable requirements and defined sequencing, such as construction, engineering, or regulated technology system upgrades. Its strengths—comprehensive planning, clear milestones, and traceable documentation—are essential where auditability and linear execution are paramount.
Hybrid approaches combine the strengths of both methodologies. A PMI report notes that hybrid adoption rose from 20% in 2020 to over 31% by 2023, reflecting organizations’ preference for a fit-for-purpose methodology. Hybrid models allow teams to apply Agile practices for adaptable components—such as UI or userfacing features—while maintaining Waterfall discipline for backend integrations or compliance-heavy phases.
Rather than applying a one-size-fits-all methodology, leading companies evaluate factors like regulatory risk, delivery timeline, stakeholder engagement, and resource stability. Mature governance and strong PMOs enable this flexibility, allowing each project to be matched with its most effective delivery model rather than forcing a blanket approach.
The Cost of Delay: Managing Budget and Timeline Risk in Complex Projects
Cost overruns and delays are persistent risks, even for organizations with well-established PMOs. Research from the Project Management Institute indicates that fewer than two-thirds of projects meet their original goals, timelines, and budgets, and roughly 17% fail outright
For large-scale capital initiatives, the financial impact can be significant. McKinsey’s review of over 300 “megaprojects”—each valued at over US billion—found that, on average, cost overruns approached 80% and schedule delays averaged 50%.
To limit risk and protect investment, industry leaders integrate financial controls and proactive performance monitoring early in the project lifecycle. Approaches such as Earned Value Management (EVM) enable teams to identify cost or schedule variances as soon as they emerge. Meanwhile, scenario modeling and contingency planning help sponsors stress-test funding assumptions, capacity allocations, and compliance implications before delays materialize.
Project Tools and Automation: Impact and Limits
Digital tools have become essential to how organizations manage complex project portfolios. Platforms like Microsoft Project, Smartsheet, Jira, and Monday.com enable real-time task tracking, centralized communication, and cross-functional visibility—capabilities that are especially valuable in distributed or high-volume project environments.
Automation is beginning to reshape project workflows. Intelligent assistants embedded in enterprise tools can draft schedules, generate updates, and flag potential risks. As project management tasks become increasingly supported by AI, organizations are exploring how automation can reduce administrative overhead and support faster decision-making.
While these capabilities are advancing quickly, adoption remains uneven. A 2023 Harvard Business Review analysis found that teams using AIenabled tools were more likely to meet delivery targets—particularly when automation supported human judgment rather than replacing it. Tasks like meeting summarization, dependency detection, and document generation are among the most common early use cases.
Still, automation is not a substitute for experienced leadership. For complex or high-risk programs—such as core system migrations or regulatory initiatives—critical thinking, stakeholder management, and risk interpretation remain the responsibility of skilled professionals. Without proper oversight, reliance on AI-generated outputs can obscure errors, introduce bias, or reinforce flawed assumptions.
As project management tools evolve, organizations must balance automation with accountability. Technology can enhance execution, but
it cannot replace the insight, coordination, and context that experienced project leaders bring to the table.
Talent and Project Capability
Tools can enhance delivery, but human expertise remains foundational. According to a recent PMI Pulse of the Profession® report, organizations that invest in structured development—via training, leadership coaching, and cross-functional collaboration— outperform their peers in project success rates and team effectiveness.
The World Economic Forum highlights that project leadership, analytical thinking, and teamwork rank among the fastest-growing skill priorities for 2030, emphasizing the importance of strategic capabilities beyond technical expertise. In response, leading firms are increasing leadership development for project sponsors and PMO heads to foster stronger ties with finance, risk, and operational functions.
As project complexity increases, PMOs are evolving from “policy enforcers” to strategic advisers. They are now responsible for portfolio planning, risk reporting, and linking program outcomes with enterprise priorities. Their performance indicators extend beyond delivery milestones to include value realisation, user adoption, and alignment with strategic business objectives.
Project Risk, Regulation, and Oversight
Risk management and regulatory alignment are central to delivery—especially in financial services. Projects involving customer onboarding, anti-money laundering (AML) systems, or ESG reporting must align not only with internal targets, but also with external timelines and compliance requirements.
Frameworks such as Basel III, GDPR, and the CSRD have introduced new data, control, and documentation obligations that are directly shaping project scope and milestones. A delay
in implementing ESG data systems, for example, can affect both reporting timelines and investor disclosures.
To meet this challenge, many financial institutions have integrated project and compliance platforms—ensuring that documentation, approvals, and version histories are audit-ready from day one. Some have adopted RegTech tools that provide real-time visibility into policy updates and allow compliance teams to flag issues directly within the project plan.
Resilience planning has also taken on new importance. Following the operational shocks of the pandemic, risk registers now routinely include third-party dependencies, geopolitical factors, and cybersecurity events. Project management and enterprise risk management teams are increasingly coordinating on scenario planning, vendor assessments, and continuity safeguards to reduce exposure during execution.
Closing the Execution Gap
Execution has become a business issue, not just a delivery function. Organizations with strong project capabilities are not simply completing more initiatives—they are achieving more consistent business outcomes. Their project leaders are better equipped to navigate complexity, maintain transparency, and engage stakeholders with clarity.
Transformational programs—whether focused on cloud architecture, regulatory compliance, sustainability, or operational efficiency—now carry both strategic value and reputational risk. In this environment, strong project governance, trained leadership, and well-integrated tools are no longer optional. They are prerequisites for performance.
Those who invest in execution—through talent, technology, and governance—are not just improving their delivery rates. They are building institutions that can deliver change reliably and at scale, even under pressure.
Marketing’s New Frontier: Customer Segmentation and Personalization in the Data-Driven Age
Customer segmentation and personalization have moved from the margins to the center of modern marketing strategy. As businesses rely more heavily on digital channels to engage audiences, the ability to identify, understand, and respond to customer needs with greater precision has become essential. Segmentation enables marketers to group customers based on meaningful differences—whether behavioral, demographic, or contextual—while personalization translates those insights into tailored messages, offers, and experiences. Together, they offer a practical path to more effective campaigns, stronger customer relationships, and improved marketing performance across every stage of the customer journey.
Segmentation has long been a fixture in marketing strategy, but many traditional models rely heavily on broad demographic categories such as age, gender, income, or location. While these attributes can provide a starting point, they often fail to capture the complexity of how individuals make decisions or engage with a brand. In recent years, marketing teams have shifted toward more nuanced approaches—especially behavioral segmentation, which focuses on actions rather than assumptions.
Modern segmentation strategies increasingly rely on real-time signals rather than static attributes. Browsing behavior, transaction history, and engagement patterns provide insight into customer intent, allowing marketers to act on emerging needs instead of fixed profiles. Customer Data Platforms (CDPs) play a key role in this evolution by consolidating data from across channels and touchpoints into a single view. This enables businesses to create and refine segments dynamically. According to Salesforce, 73% of customers now expect companies to understand their unique needs, making behavioral and contextual segmentation essential to meeting that expectation.
Behavioral segmentation enables companies to group customers based on how they browse, buy, respond to promotions, or engage across channels. These patterns can offer far more insight than static traits. For example, a customer who frequently engages with educational content but rarely completes purchases may require a different type of messaging than someone with a shorter path to conversion. As Salesforce explains, this approach allows marketers to identify purchasing triggers, moments of hesitation, and opportunities for deeper engagement based on real interactions.
Incorporating behavioral insights helps companies tailor messaging by timing, frequency, and content format—driving higher relevance and response rates. This strategy also opens the door to lifecycle-based segmentation, where communications shift based on a customer’s stage in their journey: from acquisition to onboarding, renewal, or retention.
Psychographic segmentation—based on values, interests, and personality traits—adds yet another layer of depth. This method has gained traction among lifestyle and consumer brands, but it’s also relevant to financial services, where trust, goals, and attitudes toward money vary widely across customer groups. When applied carefully, these insights can inform not only how a product is marketed, but how it’s designed and delivered.
In sectors such as banking and insurance, segmentation is especially critical. A millennial using a mobile budgeting tool may have vastly different expectations from a high-net-worth client managing a diversified portfolio. Identifying these needs early enables firms to tailor not just the message, but the product and channel strategy itself. Firms that segment well—and continuously update those models—can build stronger, longer-lasting relationships with their clients.
Personalization as a Marketing Capability
While segmentation helps define who customers are, personalization determines how best to engage them. It bridges insight and execution—applying what businesses know about their customers to shape messaging, timing, content, and offers in a way that feels relevant and timely. In its most effective form, personalization moves beyond isolated campaign tactics and becomes a scalable marketing capability—one that can adapt to customer preferences across channels and throughout the lifecycle.
At its core, personalization is about recognizing individual differences and reflecting them in the experience a brand delivers. That can take many forms: greeting returning users by name, recommending products based on past interactions, adjusting frequency based on engagement patterns, or tailoring financial offers to match spending behavior. When executed consistently, these touches create a sense of familiarity that builds trust and increases conversion.
The business case is well established. According to a recent Twilio Segment report, 80% of business leaders who invested in
personalization saw improved customer loyalty, and nearly two-thirds reported increases in customer lifetime value. Importantly, 69% of consumers said they are more likely to buy from brands that deliver consistent, personalized experiences across all platforms. The return is not limited to short-term gains; personalization plays a long-term role in retention, advocacy, and brand differentiation.
Financial institutions, in particular, are seeing the impact of this shift. Personalized banking experiences—such as financial wellness tips based on transaction data or savings goals customized to income and behavior—can deepen engagement without adding product complexity. In insurance, some providers are using personalization to guide customers through policy selection, adjust recommendations over time, and flag relevant add-ons based on life events or risk factors.
Real-time personalization has also become more accessible. Marketing teams no longer need to rely on static rules or manual customization. Integrated platforms can now update user profiles automatically and deliver content variations in response to live user behavior. These systems often work in conjunction with customer data platforms (CDPs), content management systems
(CMS), and analytics tools, allowing for a coordinated approach across channels—from email to web, mobile apps, and even call centers.
Yet the most effective personalization strategies are those grounded in a clear understanding of customer needs. Personalization fails when it becomes intrusive, irrelevant, or misaligned with brand purpose. Organizations that lead in this space are disciplined about when and how they use data—and prioritize helpfulness over novelty.
Operationalizing personalization at scale involves more than software. It requires strong data governance, alignment between marketing and IT, and clearly defined responsibilities across teams. Without these foundations, personalization efforts often falter—fragmented, inconsistent, or overlooked. In fact, Gartner reports that 63% of digital marketing leaders still struggle with personalization technology, even though they recognize its importance for customer engagement.
Personalization is no longer a “test and learn” capability for most organizations. It is a core marketing function, tied directly to growth targets and customer retention. As customers grow more
selective and privacy expectations evolve, the ability to personalize in a respectful, transparent, and useful way is becoming just as important as the message itself.
Use Cases in Financial Services and Insurance
Financial services and insurance providers have historically maintained detailed customer profiles, but only recently have many begun applying that data to deliver truly personalized experiences. As digital channels proliferate and customer expectations rise, institutions are deploying segmentation and personalization in ways that directly impact performance and trust.
In retail banking, personalized services now extend beyond basic product recommendations. Banks are increasingly analyzing transaction histories to suggest savings goals, surface credit products tailored to spending behavior, or issue proactive alerts when a customer approaches a budget threshold. These efforts go beyond convenience—they reshape how customers perceive value and deepen the relationship with their financial institution. Research from EVERFI found that banks offering personalized experiences can increase digital engagement by up to six times compared to traditional approaches.
In the insurance sector, usage-based insurance (UBI) programs are helping companies personalize both pricing and service. Drivers enrolled in Progressive’s Snapshot program—which uses telematics via a mobile app or plug-in device to monitor real-world driving behavior—save an average of $322 annually. Snapshot tracks braking, acceleration, and time-of-day driving to calculate a more accurate premium, making pricing more reflective of actual risk.
The impact of telematics extends beyond pricing. In the UK, The Co-operative Insurance reported a 20% reduction in
accidents among young drivers participating in its UBI program. Similarly, LexisNexis Risk Solutions found that telematics-based programs reduced serious accidents by roughly 35% among young drivers in the years following adoption. These examples illustrate how personalization through segmentation can not only improve underwriting outcomes but also drive safer customer behavior.
Wealth managers and private banks are also using segmentation to deliver more relevant services. Personalized portfolio recommendations, curated financial insights, and market alerts now reflect an individual’s investment goals, risk appetite, and communication preferences. A retiree managing post-employment income requires a different cadence and level of support than a younger client in early asset accumulation—even when their portfolios may appear similar on paper. Effective segmentation ensures that advisors can deliver the right message, at the right time, through the right channel.
Despite progress, many institutions still face operational and technical challenges in executing personalization strategies at scale. Legacy infrastructure, fragmented customer data, and regulatory constraints continue to hinder consistency across touchpoints. Nonetheless, firms that invest in modern data platforms, cross-functional coordination, and compliance-aware personalization often see measurable gains in client retention, cross-sell performance, and service efficiency.
Technology and Infrastructure Challenges
As organizations increase their investment in customer segmentation and personalization, many encounter significant barriers on the path from strategy to execution. The core of the challenge lies not in recognizing the value of personalized engagement, but in building the operational foundations necessary to deliver it consistently, securely, and at scale.
Many firms recognize the strategic value of personalization, but few are able to implement it effectively. According to McKinsey, while 71% of consumers expect personalized interactions, only 23% of companies believe they are delivering them well—revealing a substantial disconnect between ambition and execution.
Outdated infrastructure is often at the center of this gap. Legacy systems—particularly those built around siloed customer records, fragmented data sources, or inflexible marketing tools—are poorly equipped to support real-time data flows and dynamic personalization. A typical enterprise may operate separate platforms for CRM, email, customer service, social engagement, and analytics. Without a centralized architecture or a modern customer data platform (CDP), creating a single customer view becomes nearly impossible.
Data quality and governance also remain persistent hurdles. Personalization depends on accurate, accessible, and ethically collected information. Inconsistent naming conventions, duplicate records, missing data fields, and unvalidated contact preferences can erode trust and diminish the impact of personalized marketing efforts. Moreover, organizations must comply with an expanding set of data privacy laws, including GDPR, CCPA, and upcoming regulations around AI and automated decision-making. Maintaining compliance across systems and processes is both a technical and legal necessity.
Security is equally paramount. As businesses collect deeper customer insights to fuel personalization, they also increase their attack surface. A single breach can undo years of trust-building efforts. That’s why leading companies integrate cybersecurity practices directly into their segmentation and personalization architecture.
Organizational alignment—or the lack of it—is another common barrier. A report by Capgemini notes that many traditional financial institutions are “burdened by organizational and data silos” and lack the analytics and digital capabilities required to drive personalization at. These silos often stem from departmental structures that keep marketing, analytics, IT, and compliance teams isolated—making coordinated customer engagement difficult.
Despite these challenges, the path forward is clear. Organizations that invest in centralized data platforms, standardize processes, and foster collaboration across teams are beginning to close the execution gap. These firms are better equipped to deliver consistent, personalized experiences—and as a result, they often see improvements in customer satisfaction, retention, and operational efficiency.
Strategic Outlook: Personalization as a Core Capability
Personalization and
lasting customer relationships. Their future effectiveness, however, will depend on how well organizations adapt to new expectations around relevance, trust, and control—while managing the technological and ethical demands that come with more sophisticated data use.
One clear shift is toward predictive personalization. As data infrastructure improves, companies are developing the ability to anticipate customer needs based on real-time behavioral signals. Rather than reacting after a customer shows intent, forwardlooking brands are now testing preemptive offers—such as credit adjustments based on spending patterns or nudges to boost savings based on lifestyle cues.
At the same time, marketers are rethinking their approach to data sourcing. Following repeated delays and a significant reversal by Google on the full deprecation of third-party cookies, many firms are accelerating efforts to build first-party and zero-party data strategies. These rely on direct customer engagement—such as preferences, self-reported goals, or behavior within owned platforms—rather than external tracking. The focus is shifting from passive data collection to value-based data exchange.
Alongside these shifts is a rising demand for greater control and transparency. In Deloitte’s Connected Consumer Survey, 79% of consumers said they found privacy policies difficult to understand and felt they had limited ability to manage how their data is used. Consent-based design is emerging in response, including clearer opt-ins, customizable communication preferences, and personalized control dashboards. These mechanisms not only help organizations comply with regulation—they help rebuild trust.
The role of ethical AI governance is also growing in importance. As personalization strategies become more reliant on machine learning, companies face pressure to ensure fairness, transparency, and oversight in their algorithms. Organizations aligned with frameworks such as the World Economic Forum’s AI principles are working to establish model accountability standards and reduce unintended bias in automated decision-making.
Organizational agility will be just as important as infrastructure. Effective personalization demands alignment between marketing, IT, data governance, and compliance teams. Companies that can foster this collaboration and move quickly to act on customer signals will have a clear competitive advantage. Those that remain siloed or reactive risk falling behind, regardless of the tools they implement.
As these trends converge, personalization is no longer just a means of driving engagement—it is a driver of brand credibility and long-term growth. For financial services and insurance providers in particular, the ability to deliver relevant, ethical, and usercontrolled experiences will become a defining factor in customer loyalty and market leadership.
The Retirement Reckoning:
Are We Ready for the 100-Year Life?
Average global life expectancy has increased from 32 years in 1900 to more than 71 years today, with many developed nations now exceeding 80. By 2050, the number of people aged 60 and over is expected to reach 2.1 billion—more than triple the total in 2000 and over 20% of the global population.
This demographic shift is already straining financial systems. As populations age, many governments are experiencing higher entitlement costs and slower revenue growth. In the United States, federal analysts have projected that demographic shifts will make long-term debt servicing more difficult.
At the individual level, the risk of outliving retirement savings known as longevity risk—is becoming more acute. With defined benefit pensions in decline and life expectancy increasing, households are bearing more financial responsibility for retirement. Pension funds and insurers are also under strain, particularly where low interest rates have reduced their ability to generate returns. The IMF has identified longevity risk as a growing challenge for the long-term sustainability of retirement systems.
Japan’s Ministry of Finance has supported deferred annuities to provide retirement income beyond age 85. In Europe, insurers have trialed pooled-risk contracts and longevity-linked bonds. Canadian fintechs are incorporating automatic savings increases and contribution nudges into workplace pension tools.
Pensions Under Pressure
Defined benefit pensions have sharply declined in the United States. As of 2023, only 15% of private-sector workers had access to a DB plan, while 67% had access to a defined contribution plan, such as a 401(k). Participation in DC plans remains uneven, with fewer than half of eligible workers actively contributing. In Europe,
legacy DB schemes in countries like the UK and the Netherlands have largely closed to new entrants, accelerating a shift toward individualised DC systems
Access to pensions remains limited in many regions. In low- and middleincome economies, fewer than 20% of workers are covered by any formal retirement scheme. Even in advanced markets, millions remain excluded. In the UK, part-time and low-income workers often fall below the earnings threshold required for automatic enrolment.
Pension adequacy is also a rising concern. The OECD Pensions Outlook 2024 notes that most systems fall short of replacement rate targets. Nearly half of working-age adults in the UK—around 45%—are not saving for retirement. The UK’s legal minimum pension contribution stands at 8%, split between employee and employer. Several reviews—including by the Pensions Commission—have raised concerns that this rate is too low to ensure adequate retirement income, particularly for younger or lowerpaid workers. Since the introduction of automatic enrolment in 2012, participation has increased significantly: by 2023, 88% of eligible employees were enrolled in a workplace scheme. Total contributions reached £114.6 billion in 2021, but average savings levels remain modest—especially among part-time and low-wage earners.
Public pension models across Europe and Asia are also under review. ermany’s pay-as-you-go pension model relies on today’s workers funding today’s retirees. The country’s old-age support ratio has dropped from 4:1 in 2010 to roughly 2:1, and is projected to fall further in the coming decades. In response, the government has announced a €200 billion stabilization fund to help subsidise future pension liabilities without immediately raising the statutory retirement age.
In Japan, nearly 29% of the population is over age 65—the highest proportion among OECD countries. The government has raised pension eligibility ages and expanded tax incentives for individual retirement
accounts in an effort to increase private savings and reduce longterm pressure on the public system.
Australia’s superannuation scheme mandates employer contributions but still struggles with coverage gaps among informal workers. In Brazil, reforms in 2019 curtailed early retirement incentives. South Africa continues to rely on a meanstested old-age grant as its primary support for older adults without formal coverage.
Retirement inequality remains most visible in gender outcomes. Women in the UK hold 48% less pension wealth than men at retirement age. The gap reflects longstanding labour market patterns, including lower average earnings and a higher concentration of women in part-time work. Many of these jobs do not meet the income threshold required for automatic enrolment into workplace pensions.
In response, the UK has reconvened its Pensions Commission, with new proposals expected by 2027. Across the European Union, governments are raising statutory retirement ages and adjusting benefit formulas, while expanding defined contribution coverage to ease pressure on public systems.
The global pension model—built for shorter careers and defined benefits—is being reshaped to reflect longer lives, more fragmented work, and the growing need for individual resilience.
Behavioral Barriers to Retirement Readiness
Many people struggle to save enough for retirement, not because they lack awareness or access, but because persistent behavioral biases shape their decisions.
Present bias—the tendency to prioritize immediate spending over long-term benefits—leads many to delay or underfund retirement savings. Saving requires forgoing something today in exchange for security decades from now, a tradeoff that often feels abstract. Research from the National Bureau of Economic Research suggests that addressing this bias could improve long-term savings by as much as 12%
Status quo bias keeps savers stuck at low contribution levels. Most employer-sponsored plans still default to 3% of pay, and without an external prompt, few participants revise their rate—even if it’s insufficient for long-term needs.
Optimism bias leads people to assume they will earn more, work longer, or spend less than they actually do. One study from Wharton’s Pension Research Council highlights how exponential growth bias—the tendency to underestimate the power of compound interest—can lead to persistent under-saving, especially when paired with unrealistic assumptions about income or retirement age.
Loss aversion also shapes behavior. Many savers are more sensitive to short-term market dips than to the long-term erosion of purchasing power. As a result, they favor capital preservation
strategies and avoid equities—even when long-term outcomes suggest a diversified portfolio would serve them better.
These behavioral patterns—deeply rooted, well-documented, and often unconscious—form the backdrop against which all retirement planning must operate. While financial products continue to evolve, their effectiveness ultimately depends on how well they account for the way people actually make decisions.
Rethinking Wealth Timelines and Financial Products
The old notion of retiring at 65 and spending a predictable two or three decades in drawdown is no longer realistic. People are living longer, working differently, and facing financial decisions that span into their 80s and 90s. The structure of retirement planning—and the products that support it—are being retooled to meet that reality.
Phased Retirement and the End of the Fixed Finish Line
Retirement no longer follows a fixed cutoff. Many older workers phase out of full-time employment gradually, continuing as parttime staff, consultants, or entrepreneurs. 67% of Gen X and 56% of millennials say they prefer a flexible retirement rather than a onetime exit. With 4.2 million Americans turning 65 each year through 2027, the notion of a fixed retirement date is becoming increasingly impractical—both individually and systemically. Financial planners are beginning to frame retirement age and life expectancy as variables, not assumptions—a recognition that the traditional life stages no longer apply cleanly.
Withdrawal Horizons Are Stretching
Auto-escalation strategies—originally designed to increase savings rates in workplace plans—are now being adapted for the decumulation phase. By gradually adjusting withdrawal amounts over time, these features help retirees align spending with real-life income needs and reduce the risk of early depletion.
The 4% rule once offered a simple answer to a complex question: how much can a retiree withdraw annually without running out of money? But that formula was built for a 30-year horizon. As Dunham & Associates notes, many retirees today face drawdown periods of 35 to 40 years. Planners are responding with dynamic withdrawal strategies that adjust for market shifts, income demands, and changing health status.
Income Products for Extended Lifespans
Deferred annuities and Qualified Longevity Annuity Contracts (QLACs) are gaining traction as back-end income strategies for retirees. SECURE Act 2.0 raised the QLAC purchase cap to $200,000, broadening access. Meanwhile, the market has shifted away from traditional fixed annuities toward indexed and deferred products that balance growth with downside protection. A recent study suggests that even a 10–15% allocation to deferred annuities can materially improve retirement income durability. Demand continues to rise: annuity sales hit a record $432 billion in 2024, reflecting a broader shift toward income guarantees later in life.
Countries including Japan, Canada, and several EU members are testing pooled-risk products such as tontines and longevity bonds, which aim to distribute both investment and lifespan risk across groups rather than individuals.
Shifts in Planning, Platforms, and Advice
The advisory model is changing, too. Planners are increasingly modeling 40- to 50-year financial plans, integrating variables like healthcare costs, tax sequencing, and partial employment. According to EY, the global retirement savings gap is projected to exceed $400 trillion by 2050, a signal that longevity is not just reshaping personal plans, but entire financial systems. Reverse mortgages and equity release tools are gaining popularity among older homeowners seeking to unlock cash without having to liquidate their assets. Fintech platforms are also redesigning retirement infrastructure. In addition to automating withdrawals and rebalancing, many now embed behavioral nudges—reminders to review income projections, prompts to delay withdrawals, or alerts based on spending behavior. Robo-advisors are playing a key role in this shift, offering ongoing portfolio adjustments, personalized income advice, and tools that simulate spending scenarios over multi-decade timelines. These features, according to the World Economic Forum, represent a shift toward personalized income planning that can evolve in real-time as retiree needs change.
Defined-contribution platforms are introducing in-plan income solutions such as managed payout funds and hybrid target-date options, providing more flexibility for retirees who want income continuity without locking assets into traditional annuities.
Intergenerational Wealth – Transfer or Tension?
A historic transfer of wealth is underway. Over the next two decades, Baby Boomers and the Silent Generation will pass down approximately 84 trillion dollars in assets, with $72 trillion expected to go directly to heirs. Millennials are projected to receive the largest share, with Gen Z expected to inherit around $15 trillion globally. But this handoff is more complex than it appears. Differences in values, digital fluency, and financial experience are reshaping how wealth is managed—and by whom.
Financial Institutions Respond to a Generational Shift
Traditional wealth management models were built for older clients with long-standing advisor relationships. But 70% of heirs change or fire their financial advisor after inheriting, prompting firms to rethink client retention strategies. Institutions are developing intergenerational continuity plans, onboarding younger heirs earlier, and broadening offerings to meet their expectations for transparency, accessibility, and values alignment.
Digital-native clients are accelerating the shift. Mobile-first platforms, goal-based dashboards, and AI-assisted portfolio tools are now essential features. According to Javelin Strategy, younger clients expect real-time visibility and control—features legacy platforms weren’t designed to deliver. And with up to 40% of Gen Z and Millennial heirs reporting distrust in traditional advisors, technology alone isn’t enough. Engagement must be paired with shared values.
ESG Priorities and the Rise of Values-Based Investing
Younger generations are redefining portfolio objectives. Over 90% of Millennials express interest in ESG or impactaligned portfolios, often prioritizing climate, diversity, or ethical governance alongside financial performance. Advisors are adapting by integrating ESG funds, impact metrics, and shareholder engagement strategies into wealth plans. Inheritance conversations increasingly focus not just on capital, but on legacy—what wealth enables and what it represents.
Digital Assets and Inheritance Innovation
The rise of digital assets adds further complexity. Cryptocurrencies, NFTs, and tokenized real estate are more likely to be held by younger clients, yet estate planning for these assets remains underdeveloped. Platforms like Trust & Will and Mezzi are working to close this gap with AI-powered digital vaults, secure key storage, and automated inheritance workflows. These tools help ensure that digital legacies—wallets, passwords, and asset logs—are preserved and accessible.
But infrastructure still lags behind ownership. Legal frameworks for digital inheritance vary widely, and families often struggle with access rights, security protocols, and tax implications. Advisory firms now face the challenge of incorporating digital estate plans into their broader intergenerational strategies.
Friction Between Generations
The wealth transfer is not frictionless. Bank of America research shows that many younger heirs prioritize liquidity, entrepreneurial ventures, and social impact, while older generations emphasize stability and long-term preservation. These differences often surface during discussions around asset allocation, spending priorities, and charitable intent.
Communication adds another layer. Mismatches in style—text versus email, dashboards versus paper reports—can create misunderstandings. In some families, inheritance becomes less a conversation and more a point of stress. SEI Insights notes that wealth advisors are increasingly stepping into mediator roles, helping families bridge generational divides with shared planning frameworks.
Longevity Finance as a New Discipline
The implications of longer lifespans extend far beyond healthcare or demographics—they demand a wholesale redesign of the financial system. As retirement stretches from a phase of life into an era of its own, savings behavior, product design, wealth transfer, and institutional priorities must evolve in parallel. The challenges explored in this report—from behavioral inertia to generational friction—are not edge cases. They are now the norm.
Banks, insurers, asset managers, and governments must collaborate to build frameworks suited to a 100-year financial life. This includes rethinking contribution structures, expanding postretirement planning, aligning investment strategies with longevity risk, and ensuring that financial inclusion spans generations and platforms.
Longevity is no longer a niche topic in wealth management or policy—it is a foundational design challenge. From product architecture to regulation, what was once a demographic footnote must now become a primary driver of economic planning.
Predicting and Preventing Customer Churn in Retail Banking
Customer churn is one of the most consequential threats facing retail banks today. A departing customer doesn’t just represent a loss of current revenue—it also erodes the bank’s long-term growth potential, referral network, and share of wallet. In today’s digital environment, where switching providers can be done in seconds, traditional loyalty no longer serves as a guarantee of retention. The cost implications are striking. Research shows that a modest 5% reduction in churn can boost profitability by up to 95%, depending on the product mix and lifecycle stage of the customer. At the same time, acquiring a new customer remains five to six times more expensive than retaining an existing one.
These dynamics are forcing banks to adopt a more predictive, data-driven approach to customer retention. Success no longer depends solely on offering competitive interest rates or new digital features. Instead, it hinges on a bank’s ability to anticipate churn before it happens, understand the drivers behind customer dissatisfaction, and take timely, personalized action. This requires moving beyond siloed data and static reporting dashboards. Today, machine learning (ML) is emerging as a critical tool in this transformation—equipping banks with the speed and intelligence needed to protect relationships and preserve revenue.
Rather than relying solely on traditional metrics such as account balance or branch visit frequency, ML models analyze thousands of structured and unstructured data points, including digital
behavior, transaction sequences, complaint logs, and even customer sentiment. These models are capable of uncovering early signals of disengagement that human analysts—or even rule-based systems— often miss. Sophisticated algorithms are being deployed not only to flag customers at risk of attrition, but to identify why they're likely to churn. These insights help banks proactively tailor retention strategies, improve satisfaction touchpoints, and prioritize outreach based on risk levels and revenue impact.
Leading banks are already using these technologies to target churn before it materializes. For example, when an AI model detects declining login frequency, reduced transaction volume, or recurring unresolved complaints, it can trigger alerts to customer relationship managers—prompting them to intervene with a service call, fee waiver, or tailored product offer. In other cases, the model may suggest preemptively adjusting the customer’s fee structure or providing access to loyalty benefits. The ability to act early, based on data-driven predictions, often makes the difference between preserving and losing a relationship.
As the competitive pressure intensifies and customer expectations grow more nuanced, banks that invest in predictive churn solutions are gaining a measurable advantage. They not only retain more customers but also strengthen their brand reputation, reduce marketing costs, and improve overall customer lifetime value. Machine learning is not just a technical upgrade—it’s becoming the foundation for relationship-based banking in a digital era.
Predictive Models and Algorithmic Accuracy
Many banks are now moving from passive analysis to proactive retention using advanced predictive modeling techniques. These models can process massive datasets in real time, identifying risk patterns far earlier than traditional approaches. One of the most widely used algorithms in this context is the Random Forest classifiers, which operates by combining the output of multiple decision trees to improve accuracy and reduce overfitting. In a churn prediction study focusing on retail banking clients, Random Forest achieved an accuracy rate of 87.5%, outperforming older methods such as logistic regression and basic neural networks.
What makes Random Forest appealing in financial services is its ability to handle noisy, high-dimensional data. For banks dealing with diverse customer behaviors across savings, credit, mobile, and digital channels, this flexibility is critical. It allows institutions to model real-world churn triggers such as reduced ATM usage, missed credit card payments, and declines in mobile app interactions.
However, precision alone is not enough. As machine learning becomes more deeply integrated into decision-making, explainability is equally essential. This is where SHAP (SHapley Additive Explanations) has gained traction. SHAP helps quantify the importance of each variable in a prediction, ensuring that outputs can be clearly interpreted by analysts, compliance teams, and regulators. For example, if a model predicts a high likelihood of churn, SHAP can reveal that the result was most influenced by a recent drop in account activity or an unresolved complaint. This transparency allows customer-facing teams to respond appropriately, with context.
In addition to these interpretable models, banks are exploring deep learning approaches that capture behavioral dynamics over time. LSTM (Long Short-Term Memory) neural networks are particularly effective in this area. These time-series models are designed to detect long-range dependencies in customer behavior, such as gradual disengagement. In one implementation, LSTM outperformed static classifiers by improving lift by over 25%, showing particular strength in identifying customers whose churn risk builds incrementally over weeks or months rather than in response to a single event.
Other emerging techniques include gradient boosting machines and ensemble hybrid models that combine multiple algorithm types for improved generalization. Some banks are also experimenting with reinforcement learning to continuously optimize customer retention actions, adjusting strategies based on feedback and campaign performance.
The breadth of model choices reflects the evolving maturity of churn analytics across the sector. But regardless of the specific technique, one theme remains constant. The most successful implementations are those embedded within daily operations,
designed not just to score customers but to inform real-world retention workflows. Predictive models are now informing customer service protocols, guiding automated messaging, and shaping personalized financial advice.
Enriching Models with Behavioral Signals
To strengthen the predictive power of churn models, many banks are now expanding the types of data they analyze. While structured information such as balance history, loan repayment records, and product tenure remains valuable, it often tells only part of the story. More nuanced churn signals frequently emerge from behavioral and contextual data, which provides real-time insight into how customers interact with their bank.
A growing number of institutions are now incorporating digital engagement data—such as app usage frequency, login patterns, device switching, and navigation drop-offs—to capture subtle signs of disengagement. For instance, a decline in mobile app activity or sudden changes in digital banking habits can signal dissatisfaction even before a customer contacts support or submits a complaint. When processed in aggregate, these signals become a powerful early warning system.
In one study of South African banking customers, researchers analyzed over 1.7 million social media posts to identify common customer concerns, sentiment fluctuations, and brand perception shifts. The analysis revealed that topics related to poor digital experiences, delays in support response, and unexpected account fees were strongly correlated with eventual account closures. When this social sentiment data was integrated with transactional profiles, model accuracy improved significantly.
This is where feature engineering becomes critical. Recencyfrequency-monetary (RFM) metrics—long used in marketing analytics—are being adapted for banking churn models. Recency tracks the time since the last customer interaction, frequency measures the number of transactions or engagements in a given period, and monetary value captures the average spend or account balance. When engineered alongside digital engagement data, these features help banks prioritize interventions based on risk and customer value.
Deep learning models such as LSTM networks, which are capable of learning sequential behaviors, are particularly useful for this type of data. They can track temporal changes in how users access services and identify patterns that might indicate frustration, such as frequent failed login attempts or repetitive navigation loops. For example, a customer who repeatedly tries to initiate a loan application but abandons the process at the same screen may be experiencing friction that could lead to attrition. LSTM models can detect this and prompt timely outreach.
What distinguishes these enriched models from traditional scoring systems is their integration with real-world operational tools.
Rather than serving as standalone risk dashboards, churn insights are increasingly embedded directly into customer relationship management (CRM) platforms. This enables frontline employees to view churn risk scores, contributing factors, and suggested actions in real time—allowing for more informed and personalized customer engagement.
Some banks are even automating these responses. When a churn risk score exceeds a defined threshold, the system might automatically trigger a retention campaign through email, SMS, or app notification. Others use the insight to escalate cases to a dedicated retention team who can follow up with tailored offers or service recovery steps.
The combination of diverse data, intelligent modeling, and workflow integration is transforming churn prevention from a back-office analytics function into a frontline business capability. As competition intensifies, this kind of real-time behavioral intelligence is helping banks move beyond reactive retention and toward proactive relationship management.
Operationalizing Predictive Retention
Translating machine learning insights into measurable churn reduction requires more than selecting the right algorithm. Operationalizing predictive retention involves aligning people, systems, and processes around the model’s outputs. Many banks struggle not with the accuracy of their churn models, but with how effectively they embed them into day-to-day operations. Without integration into customer service platforms and campaign management tools, even the most sophisticated models can fail to deliver results.
Implementation begins with the creation of clean, high-quality datasets. Churn models are particularly sensitive to class imbalance, where most customers do not churn and the signalto-noise ratio is low. To address this, banks often use techniques such as SMOTE (Synthetic Minority Over-sampling Technique) or adaptive sampling to ensure training data reflects the right balance of churned versus retained customers. Proper feature selection is also essential, with top-performing models often relying on a combination of structured financial data and behavioral indicators extracted from mobile and web platforms.
From there, the output of the model must feed directly into decision systems. In some institutions, this means integrating churn scores into the CRM platform where relationship managers can view realtime risk indicators alongside client profiles. In others, it powers dynamic marketing workflows that adjust offers based on churn
probability, customer value, and historical response rates. The best implementations go further, combining churn prediction with nextbest-action engines to recommend the most appropriate intervention— whether that’s a service call, loyalty offer, or temporary fee waiver.
Organizationally, banks that succeed in churn prevention often establish cross-functional retention squads composed of data scientists, marketers, digital operations, and frontline relationship managers. These teams ensure churn signals are interpreted accurately and converted into customer-facing strategies. They also help monitor the model’s real-world performance over time, calibrating predictions against actual retention outcomes and adjusting variables as necessary.
In one widely cited example, a global bank deployed an AI-powered churn model that used a combination of demographic factors, complaint history, and transaction patterns to flag high-risk clients. When the model identified elevated risk, it triggered a workflow that prompted human advisors to reach out with tailored offers and service adjustments. As a result, the bank was able to reduce churn rates by several percentage points in key customer segments. The architecture and open-source methodology behind the initiative were later made publicly available on GitHub, helping other institutions build similar frameworks.
Churn management models must also operate within a framework of trust and compliance. Legacy systems can limit a bank’s ability to deploy models in real time, while regulatory oversight requires explainable outputs and rigorous data governance. This is especially true when models rely on behavioral or third-party data, which may fall under privacy laws or require explicit customer consent. Institutions that invest in modular data infrastructure, clear audit trails, and governance workflows are better equipped to handle these risks and scale predictive models across the business.
Transparency remains central to maintaining regulatory alignment. Model explainability tools such as LIME (Local Interpretable ModelAgnostic Explanations) and SHAP are increasingly used to satisfy
regulator demands. These frameworks allow internal audit teams to trace model outputs back to specific variables and ensure that decisions—especially automated ones—can be justified.
Ultimately, the goal is not just to predict churn but to enable personalized, scalable action. The true return on investment comes when machine learning insights are tightly woven into service design, campaign logic, and customer experience delivery. Banks that approach churn prevention as an enterprise capability— not just a technical initiative—are seeing stronger retention, higher customer satisfaction, and a clear competitive edge.
Real-Time Intelligence and Human-AI Collaboration
As churn modeling matures, the future of customer retention in banking is being shaped by speed, adaptability, and intelligent collaboration between humans and machines. The most promising developments involve real-time data processing, tighter integration between digital and physical touchpoints, and a shift away from purely predictive analytics toward prescriptive and adaptive models.
Banks are increasingly adopting streaming data architectures that enable them to process customer signals as they happen. These platforms ingest real-time events—such as card usage, online session length, chatbot interactions, or app navigation behavior—and match them against model predictions. When churn probability spikes, the system can immediately trigger a targeted message, alert a relationship manager, or adjust a service workflow without delay. The goal is not just to detect risk but to respond to it while there is still time to retain the customer.
At the same time, collaboration between banks and fintechs is accelerating model innovation. Fintech platforms often offer modular AI tools that allow for rapid experimentation and deployment, particularly in areas like customer journey analytics, loyalty scoring, and alternative data integration. Some banks are embedding third-party churn APIs into their core infrastructure,
using these tools to enhance internal capabilities without rebuilding their entire architecture.
This is also contributing to the rise of hybrid models, where artificial intelligence works alongside human advisors rather than replacing them. For example, a machine learning model may rank customers by churn risk and suggest possible interventions, but it is the advisor who determines whether to call, offer a retention package, or flag a broader relationship concern. This balance between automation and human discretion helps maintain empathy and personalization—especially for high-value customers where relationships are critical.
Research supports this approach. A study published by MDPI found that AI systems paired with domain expertise significantly outperformed standalone algorithms when it came to retention, cross-sell, and customer satisfaction scores. The best outcomes were achieved not by fully automating retention strategies, but by using AI to augment human intelligence and reduce information overload.
As these technologies evolve, ethical considerations and governance frameworks will remain central. Responsible AI policies, consent-based data practices, and continuous monitoring for model bias will be essential in maintaining both customer trust and regulatory alignment. Banks that adopt these safeguards early will be better positioned to scale their churn strategies sustainably and avoid reputational risk.
In the end, churn prediction is no longer just a statistical exercise. It has become a strategic discipline that blends behavioral science, digital engineering, customer experience design, and financial analytics. Banks that embrace this complexity—equipped with the right tools, governance, and cross-functional collaboration—are not only reducing attrition. They are building deeper, more resilient relationships that unlock long-term growth in an increasingly volatile market.
Redefining Relationship Banking: Can Human-Centric Service Compete with Hyper-Personalised AI?
Until the 1990s, retail banking was still a face-to-face experience. Customers typically visited a branch to manage routine finances, secure a mortgage, or apply for business credit—often working with an advisor they knew by name. In Germany and Switzerland, savings and private banks have gained a reputation for cultivating multi-generational client relationships in business and wealth management, according to Treasury Management. These relationships were grounded in consistency, community ties, and a banker’s ability to understand context beyond numbers.
That model began to shift with the rise of ATMs, call centers, and online banking, which introduced convenience but reduced the need for in-person interactions. As operational efficiency became the focus, many banks moved to centralized credit decisions and standardized risk models, gradually replacing local judgment with algorithmic scoring shift toward centralized decision-making. By the early 2000s, banks had adopted omnichannel delivery, allowing customers to initiate transactions online or via mobile and complete them at a branch—with seamless continuity across platforms. At the same time, the role of the relationship manager was changing. Advisors were increasingly expected to deliver personalized support using CRM systems, behavioral data, and customer insights—not just memory and personal rapport, as outlined in McKinsey’s analysis of omnichannel models.
To support this shift, many institutions began to redesign their branches into consultative environments—featuring private rooms, open-plan advisory zones, and digital self-service tools—so that staff could focus on complex decision-making rather than routine transactions. This reinvention of branches into advisory hubs reflects a broader strategy: positioning the physical bank as a space for relationship-building, not just service fulfillment. In Asia, the transition took a different form. Many consumers in China, Singapore, and Hong Kong skipped desktop banking entirely and moved directly to mobile-first platforms. Yet despite rapid digitization, affluent clients in Asia continue to expect human-led advisory for legacy planning and cross-border finance according to Krungsri Research
In the United States, national banks focused on expanding digital infrastructure, while community banks and credit unions continued to differentiate through localized, relationship-driven service. In sub-Saharan Africa, institutions like M-Pesa developed banking
ecosystems through agent-based networks and mobile interfaces, creating trusted service models without physical branches—an alternative form of relationship banking adapted to infrastructure constraints.
Across all markets, the delivery methods have changed, but the demand for human guidance in moments of complexity, emotion, or uncertainty has not disappeared. Whether navigating succession planning, SME lending, or cross-border strategies, many clients still turn to real advisors when the stakes are high.
The question now is whether that human element can remain competitive— or even relevant—as banks deploy ultra-personalized service powered by AI platforms that claim to know customers better than any individual banker ever could.
The Rise of Hyper-Personalised AI in Banking
The digital transformation of banking is no longer just about moving transactions online—it’s about reengineering how institutions understand, anticipate, and respond to individual customer needs. At the center of this evolution is hyper-personalisation, a strategy powered by artificial intelligence, machine learning, and real-time behavioral analytics that enables banks to tailor experiences to a segment of one.
Unlike traditional segmentation models, hyper-personalisation draws from a broad and dynamic set of signals—including transaction history, spending patterns, life events, device usage, and even contextual cues like location or time of day. The goal is to deliver interactions that are not only relevant, but proactive and timely—often before the customer realizes they need assistance. As Krungsri Research notes, this shift allows banks to create AIdriven engagements that feel intuitive and situation-aware, moving beyond demographics into real-time emotional and financial context.
Several technologies work in concert to enable this model. Machine learning algorithms continually update customer profiles based on behavioral data and refine predictive accuracy with every interaction. Next-best-action engines evaluate real-time context—such as recent credit card use or a declined purchase—to suggest targeted offers or advice. Meanwhile, AI-powered chatbots and virtual assistants provide dynamic recommendations within natural conversations, blending conversational UX with backend personalization.
The result is a new kind of engagement. Digital banks like Monzo and
Revolut use real-time data to issue spending alerts, saving reminders, or nudges tied to bill payment cycles. Roboadvisors such as Betterment and Wealthfront adjust portfolios automatically based on changes to life goals or risk preferences— sometimes prompted by life events like the birth of a child or career shift. In the Middle East, ila Bank’s AI-powered rewards program uses Mastercard data to deliver real-time offers aligned with unique spending habits.
The approach varies across global markets. In China and Southeast Asia, “super-app” ecosystems like WeChat and Grab Financial have normalized embedded financial services, where payments, loans, insurance, and savings are all accessed through a single app—with personalization baked into every screen. In Europe and the U.S., open banking frameworks and API-based ecosystems enable richer personalization by pulling data from multiple institutions. And in Africa, mobile-first institutions are using alternative data points—such as mobile airtime top-ups—to personalize credit and financial guidance, extending services to populations with no formal banking history.
Hyper-personalised AI is not simply a customer service layer—it is rapidly becoming a core banking strategy. At Starling Bank, customers can search “how much I’ve spent at McDonald’s” and receive AI-generated visualizations of spending history. At JPMorgan Chase, an internal generative AI assistant called “Coach AI” is now used by over half the workforce to deliver research, anticipate client questions, and accelerate onboarding— contributing to a year-on-year sales uplift of approximately 20 percent.
The business case is equally strong. BCG and Wipro both highlight measurable gains in engagement, marketing efficiency, and revenue uplift when personalization is implemented across channels. Banks that shift from cohort-based targeting to micromoment nudging can reduce acquisition costs by up to 50% and improve cross- and upsell effectiveness by 5-15%.
Of course, challenges remain. Scaling AI across legacy infrastructure, ensuring data privacy, mitigating algorithmic bias, and building the internal culture to support agile decision-making are all ongoing hurdles. But the direction is clear: the traditional model of mass messaging and one-size-fits-all offers is giving way to deeply individualized engagement.
In this new paradigm, the bank-customer relationship is being redefined—not through more human interaction, but through machines designed to mimic human attentiveness. Whether this ultimately complements or competes with human advisors is the question that lies at the heart of the evolving relationship banking model.
What Human-Centric Relationship Banking Still Offers
Despite rapid advances in hyper-personalised AI, there are dimensions of banking where digital precision still falls short.
When money intersects with emotion, ambiguity, or long-term consequences, many customers prefer the steady presence of a trusted advisor. Human-centric relationship banking continues to offer essential value—especially in areas where empathy, judgment, and deep contextual understanding are critical.
At the core is trust—a quality that remains difficult to replicate algorithmically. Customers are more willing to disclose their hopes, fears, and financial anxieties when they feel genuinely understood. Human advisors can pick up on non-verbal cues, emotional tone, and unspoken hesitation—signals that don't show up in transaction logs. Research across service industries confirms that empathy significantly improves customer trust, satisfaction, and loyalty In banking, these emotional dynamics influence not just how customers feel—but what products they adopt, and whether they stay with an institution long-term.
These relationships are especially vital in complex or high-stakes scenarios. A business owner evaluating refinancing options may be grappling with stress or uncertainty beyond the spreadsheet. A family navigating succession might face longstanding interpersonal tensions. In these moments, human advisors bring both perspective and empathy, interpreting financial decisions within personal, often messy realities.
Context matters, particularly in legacy, intergenerational wealth, or regulatory-heavy products. In Asia, Europe, and the Middle East, high-net-worth families still expect relationship managers who understand their cultural preferences, family dynamics, and philanthropic goals. Even in the U.S., community banks and credit unions continue to win loyalty through continuity of service and personalised attention—especially among older clients who value human connection over digital efficiency.
Customers also rely on human guidance in moments of uncertainty. When markets shift or life circumstances change, many want direct, plain-language explanations, conversations that weigh trade-offs and consider long-term outcomes. A study from Emerald Insight found that frontline staff who demonstrate empathy and responsiveness significantly increase trust, which in turn drives retention and engagement.
Leading banks are investing in these soft skills. Relationship managers are being trained in emotional intelligence, cultural fluency, and active listening—because clients consistently report that authentic human connection makes a difference. According to Accenture, empathy-led communication can increase retail banking revenue by up to 9%, highlighting the commercial value of emotionally intelligent service.
Still, there are barriers. In private banking, relationship managers spend up to 70% of their time on administrative tasks, limiting their capacity to focus on client needs. To address this, banks are using AI not to replace human advisors, but to support them—automating routine work so they can prioritize high-touch engagement.
Relationship intelligence platforms, for instance, help surface key insights before client meetings, flag sentiment shifts, and suggest timely follow-ups, allowing human connection to scale without losing authenticity.
The takeaway isn’t that human bankers must compete with AI—but that their role is evolving. As technology handles routine queries and predictive nudges, relationship managers are left with what machines still struggle to master: nuance, emotion, and human judgment. For many customers, especially when the stakes are high, that’s what makes a relationship real.
Where the Battle Is Happening: Key Touchpoints
The contest between human advisors and AI-powered banking solutions is playing out in real time, at every major customer touchpoint. From digital nudges to portfolio rebalancing, these moments reveal not just how banks serve their clients, but how they are shaping the future of trust, guidance, and engagement.
Retail Banking: Mobile Nudges vs. Human Coaching
For most consumers, day-to-day banking has shifted into the palm of their hands. Mobile apps now offer instant account updates, smart notifications, and hyper-personalised insights. These nudges are driven by machine learning models that process spending behavior in real time to suggest actions like saving, budgeting, or setting aside funds for specific goals.
Canada offers a strong case study in how traditional banks are adopting fintech-style intelligence while preserving human support. At the Royal Bank of Canada, the NOMI digital assistant analyzes individual transaction patterns to proactively notify users about unusual activity, suggest savings opportunities, and predict cash flow shortfalls—all within a platform that still provides access to in-person service.
Elsewhere, tools like Clayfin’s Spinach engine demonstrate how AI can anticipate short-term financial pressure and guide customers toward preemptive action. According to Publicis Sapient, today’s consumers increasingly expect their financial providers to “know them” as intuitively as Netflix or Spotify, setting the bar for what personalised banking means.
Still, many customers, especially those navigating financial stress or life transitions, prefer the steady guidance of a human coach. Unlike push notification, a live advisor can explain the trade-offs of debt consolidation, guide a client through recovering from a financial shock, or motivate saving in a way that feels relational rather than transactional. Platforms like Chime, Monzo, and Nubank may offer algorithmic nudges, but several banks continue to offer access to real people through video appointments or branch-based coaching for customers facing major decisions.
In emerging markets, AI-driven mobile platforms are also being used to extend credit and savings tools to underserved populations. Personalised recommendations sent via SMS or low-bandwidth apps are helping to build financial literacy and inclusion—especially in markets like Southeast Asia, where super-apps integrate banking, commerce, and lifestyle services into a single experience.
Private & Wealth Banking: Predictive AI vs. Deep Human Relationships
At the higher end of the market, clients have different expectations. Wealth management isn’t just about automation, it’s about judgement, continuity, and trust. AI may now support everything from real-time research to tax optimisation, but most high-net-worth individuals still expect human advisors to guide their most complex financial decisions.
At JPMorgan Chase, the internal AI tool “Coach AI” helped wealth teams boost sales by 20% amid market uncertainty. Tools like this give relationship managers faster access to research and deeper visibility into client sentiment. And according to Deloitte, AI support can enable RMs to manage significantly more client relationships—raising portfolio capacity from an average of 37 to over 60—by automating background prep and compliance work.
Yet for strategic conversations—legacy planning, succession, or philanthropic strategy—clients often seek out someone who knows them personally. A PwC report found that 81% of commercial and SME clients prefer human advisors for high-stakes conversations, and that nearly a third of firms are embedding AI into advisor support—not to replace human interaction, but to strengthen it.
Canadian institutions again offer a leading example. At RBC Dominion Securities and BMO Private Wealth, clients use digital dashboards to track portfolios and receive AI-generated insights, but still engage directly with experienced relationship managers for multi-generational wealth planning and complex investment decisions. Advisors who understand a client’s cultural background or speak their language can often connect more effectively—especially when working with families that expect discretion, continuity, and sensitivity to tradition.
These frontline interactions, whether digital nudges or face-to-face strategy sessions—underscore one thing: the strongest banking experiences don’t come from choosing AI over people or vice versa, but from designing for both. That’s where the next evolution is already taking shape.
The Hybrid Model: Augmented Relationship Management
The future of relationship banking isn’t about picking sides in the AI vs. human debate. The most competitive institutions are building hybrid models—where technology supports the advisor, not replaces them. In these setups, artificial intelligence acts as a co-pilot, enhancing
judgment, improving timing, and personalizing engagement at scale, while human bankers retain control of the relationship and decision-making.
How Hybrid Relationship Banking Works
Hybrid models combine AI-generated insights with real human oversight. Relationship managers use tools that process behavioral data, surface next-best-actions, and prompt timely outreach—allowing advisors to shift their focus from admin work to strategic conversations. These systems can analyze customer transactions, detect shifts in sentiment, flag regulatory risks, and prepare pre-meeting briefings based on recent life events or portfolio changes.
Financial institutions are turning to relationship intelligence platforms that help advisors prepare for client meetings, track emotional tone, and follow up effectively. Tools like Einstein GPT, Microsoft Copilot, and Quantile’s Engagement Suite support these tasks behind the scenes—automating what was once manual and time-consuming. According to Accenture and BCG, the result is a 20–40% gain in advisor productivity and noticeable improvements in client satisfaction and compliance tracking.
Global Case Studies
In Canada, the Royal Bank of Canada’s NOMI system provides clients with AI-driven savings recommendations, while allowing human advisors to manage planning and long-term strategy. Similarly, BMO has appointed a Chief AI & Data Officer and introduced an AI assistant for field underwriting, aimed at streamlining information access and supporting advisors. CIBC has launched a proprietary GenAI platform, CIBC AI, which automates meeting preparation, internal summaries, and recurring documentation. These tools help reduce administrative workload and free up wealth advisors to focus on complex planning, relationship-building, and long-term client strategy.
In Asia, DBS Bank has implemented an AI-driven relationship intelligence platform that flags behavioral changes—such as sudden overseas spending or new investment activity—and prompts bankers to initiate outreach with contextually relevant advice. HSBC uses a similar system that combines CRM logs with AI-powered sentiment detection to suggest optimal timing and topics for relationship manager outreach.
The Commonwealth Bank of Australia leverages behavioral modeling and real-time event detection to notify advisors of opportunities such as refinancing triggers or financial milestones—
creating a proactive client engagement model.
The same principles hold. At JPMorgan Chase, Coach AI now supports more than half of the firm’s 200,000 employees—helping advisors retrieve internal research, summarize communications, and prepare for market conversations. The platform contributed to a 20% boost in wealth management sales during volatile periods.
At Morgan Stanley, internal tools like Assistant and Debrief help advisors draft meeting summaries, flag follow-ups, and stay aligned with compliance. The firm has emphasized that these systems are meant to support—not replace—human expertise, with final decisions left to the advisor.
Why Hybrid Wins
Hybrid banking models bring together the best of both worlds:
• Advisor productivity rises, as routine data collection and reporting are handled by machines.
• Personalization becomes scalable, reaching mid-market clients—not just the ultra-wealthy.
• Outreach improves in timing and relevance, with AI helping flag life changes, market shifts, or behavioral signals.
• Compliance is enhanced, with interaction tracking and AIaided documentation supporting regulatory standards.
What Banks Must Still Address
Implementing hybrid models at scale isn’t plug-and-play. Banks need unified data infrastructure, cross-platform CRM integration, and internal alignment across business lines. Advisors also need to trust the tools. Successful change management requires transparency—so that bankers understand how AI supports, not undermines, their work.
Client perception matters too. If customers feel their advisor’s insight is being scripted by software, the relationship weakens. Opt-in personalization, explainability, and thoughtful design help preserve transparency and trust.
When done right, hybrid models don’t dilute human relationships; they elevate them. The banker becomes more effective, more relevant, and more available. And the client benefits from the speed, insight, and personalization of AI—delivered through a voice they already trust.
Buy
Now, Pay Smarter: Is the BNPL Model Finally Growing Up?
A few years ago, it became nearly impossible to shop online without being offered the option to “pay in four.” Klarna, Afterpay, Affirm, and other Buy Now, Pay Later (BNPL) providers quickly became embedded in the digital checkout process, presenting themselves as a simpler, more transparent alternative to credit cards. With no interest when paid on time, no annual fees, and instant approvals—often without a credit check—BNPL gained a foothold among younger consumers drawn to speed and ease.
Retailers benefited just as much. Offering BNPL could increase average order values by up to 85%, and 40% of users were firsttime customers, according to Capital One Shopping. During the e-commerce boom of the early 2020s, adoption surged. In the UK, more than 10 million adults used BNPL services annually by 2024, according to the Financial Conduct Authority. In the U.S., five major players originated $24 billion in BNPL loans in 2021—up from just $2 billion two years prior, according to the Consumer Financial Protection Bureau
year. Because these short-term installments often fell outside traditional credit reporting systems, lenders lacked a clear view of a consumer’s total borrowing activity.
By 2024, an estimated 86.5 million Americans had used a BNPL service, with that number expected to exceed 94 million in 2025, according to research compiled by Digital Silk. Globally, the market surpassed $309 billion in 2023 and is projected to reach $560 billion by the end of 2025, driven by platforms such as PayPal, Zip, and Apple Pay Later, which was rolled out with built-in controls linked to debit or prepaid cards.
But the same features that made BNPL attractive also made it easy to overextend. Most approvals were granted within seconds, often with no hard credit inquiry. But the ease of access also made it difficult for borrowers to manage their obligations across platforms. According to the Consumer Financial Protection Bureau, 63% of BNPL users carried multiple active loans at once, and roughly one in three used more than one provider in a single
The warning signs grew harder to ignore. Reports emerged of consumers relying on BNPL for groceries—one-quarter of American users, by some estimates—underscoring the shift from impulse purchases to everyday essentials. Some borrowers began missing payments, triggering penalty charges that added to their obligations. Reports from the Bank for International Settlements and Morgan Stanley highlighted emerging risks: BNPL debts often operated outside traditional monitoring frameworks, making it harder to assess a consumer’s full exposure and more difficult for regulators to track the flow of credit. Meanwhile, lenders began to react: FICO announced that it would begin integrating BNPL data into credit scores, while several U.S. banks blocked the use of credit cards for BNPL payments due to repayment risks.
The Consumer Wake-Up Call
BNPL’s appeal has always hinged on accessibility—quick approvals, no interest, and frictionless checkout. But as usage scaled, so did the complications. Recent surveys point to a common trend: many users are encountering problems, particularly around overspending and repayment missteps. According to Bankrate, nearly half of U.S. BNPL users reported experiencing at least one negative outcome. For Gen Z in particular, the most common issue wasn’t missed payments—but spending beyond their means, often across multiple purchases.
The Financial Conduct Authority highlighted that some BNPL users in the UK held multiple repayment plans and struggled to track payment timing or balances. Citizens Advice found that in 2024, it more than doubled the number of BNPL-related debts it helped resolve compared to 2022. Around 79% of people seeking help were struggling with affordability, and 21% required support such as food bank referrals.
Behind the aggregated data are individual stories that reflect how quickly these obligations can spiral. In one widely reported case, a U.S. shopper accumulated over $30,000 in BNPL debt, spread across multiple lenders. Like many, she assumed the fixedpayment model would be easier to manage than revolving credit— until she began missing deadlines and was hit with fees across accounts. Her case was not isolated.
Research from the Consumer Financial Protection Bureau indicates that 63% of users had more than one BNPL loan active at a time, and a third used multiple providers in the same year. Approval remained common even among borrowers with subprime credit profiles. According to Payments Dive, BNPL users tended to carry more unsecured debt than non-users— compounding the risk of falling behind.
That risk has already started to materialize. In Q1 2025, Klarna reported a 17% year-over-year rise in loan losses, attributing the increase to missed payments. A separate report from LendingTree found that 41% of BNPL users had made at least one late payment, up significantly from the prior year.
The stress is not limited to discretionary purchases. By 2025, BNPL use had expanded well beyond discretionary shopping. According to Food & Wine, 25% of Americans were using BNPL to
for groceries, up from 14% just two years earlier. Researchers at the Kansas City Federal Reserve found that borrowers facing multiple financial constraints—such as limited savings or recent credit denials—were significantly more likely to fall behind on repayment plans. Consumers with limited savings, prior credit denials, or existing delinquencies were far more likely to fall behind on payments.
Most BNPL providers do not report successful repayments to credit bureaus. As a result, borrowers who meet their obligations on time see no improvement to their credit history. But the reverse can still carry consequences. When payments are missed or accounts are overdrawn, some lenders pass the debt to third-party collectors— affecting credit scores and access to other financial services.
Regulatory Heat and Industry Response
As Buy Now, Pay Later became a staple at online checkouts, regulators across major markets began reevaluating the risks embedded in its frictionless model. What initially operated in regulatory grey zones has, over the last two years, become the subject of formal legislative reform in the UK, Australia, and the European Union—each moving to impose licensing, affordability checks, and clearer consumer safeguards.
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In the United Kingdom, the Financial Conduct Authority (FCA) is preparing to bring BNPL fully under its remit starting 15 July 2026. The upcoming regulation will treat these products as a form of Deferred Payment Credit, requiring third-party providers to secure FCA authorisation or temporary permission. Under proposals outlined in consultation paper CP25/23, firms will be required to conduct affordability assessments—even for lowvalue purchases—standardise disclosures on fees and terms, and offer access to the Financial Ombudsman Service and Section 75 protections. Anti-avoidance provisions are also included to prevent merchant-owned workarounds. UK media have noted the scale of the shift, with some outlets calling it a move to end the “wild west” of short-term credit
Across the European Union, reform is already underway. The revised Consumer Credit Directive (CCD II), adopted in October 2023, eliminates previous exemptions that allowed BNPL products to operate outside traditional credit rules. Once implemented, CCD II will require mandatory creditworthiness checks, clearer disclosure of fees and advertising terms, and harmonised consumer protections across member states. According to Oliver Wyman, providers are already revising onboarding flows, fee models, and compliance systems to meet the new requirements.
Australia, one of the most active BNPL markets, has taken a similarly firm stance. Starting 10 June 2025, all BNPL providers must hold an Australian Credit Licence (ACL) and comply with Responsible Lending Obligations under the National Consumer Credit Protection Act. The reforms also require access to the Australian Financial Complaints Authority, implement hardship provisions, and tie BNPL repayment behavior to formal credit reporting. News.com.au reports that the changes will affect millions of users and require major operational shifts by firms such as Afterpay, Zip, and Humm.
The industry’s public response has been largely supportive—at least among leading players. In the UK, both Klarna and Clearpay have welcomed the FCA’s proposals, describing regulation as a means of enhancing consumer trust and formalising the market. In Australia, providers are working to align their operations with new licensing and compliance standards. Across Europe, platforms are adjusting fee structures and transparency frameworks to comply with CCD II, highlighting widespread product changes.
BNPL 2.0 – Toward Smarter, Safer Credit
Early Buy Now, Pay Later products attracted users with simple installment plans that avoided interest charges and traditional credit screening. These services became popular among younger consumers and those with limited access to conventional credit. But as repayment difficulties and disputes about terms grew more common, providers and regulators moved to strengthen borrower protections.
Greater Transparency and Regulatory Alignment
Regulators in the UK, Australia, and the EU have responded with stricter requirements. The Financial Conduct Authority in the UK has introduced rules to make fees, repayment terms, and consumer protections more visible at the point of purchase.
In 2025, the Australian Securities and Investments Commission required BNPL providers to hold credit licenses and follow lending obligations similar to those applied to credit cards.
The EU’s revised Consumer Credit Directive (CCD II) introduced fee caps and mandatory withdrawal rights. According to the European Commission, these changes aim to standardize protections and reduce confusion across markets.
Smarter Risk Tools and Embedded Credit Checks
New BNPL platforms are using more detailed credit assessment tools. Zilch works with Experian and other credit bureaus to tailor offers based on a user’s history.
Scoring models are evolving as well. FICO plans to include BNPL data in its next updates. As The Guardian explains, this shift could improve visibility for users previously left out of the credit system.
Bank-Led BNPL and Financial Ecosystem Integration
Banks are offering installment plans through their cards and digital platforms. Financial Times reports that firms like Citi and Lloyds are using their infrastructure to provide more consistent terms and oversight. Cross River Bank has noted that banks may be better positioned to manage repayment risk.
Analysis from Accenture and JPMorgan shows that integrating BNPL into banking systems can give financial institutions broader data insights and help them personalize offers.
Designing for Financial Health
Some BNPL providers are adding features that support better repayment habits. These include alerts for upcoming due dates, budgeting interfaces, and lower fees for users who stay current on payments.
Firms like Affirm and Zilch now report repayment behavior to credit bureaus. The Consumer Financial Protection Bureau supports consistent data sharing as a way to improve access to mainstream credit.
BNPL in Everyday Channels
BNPL has moved beyond e-commerce into daily spending. Klarna now offers payment plans through DoorDash for food and grocery orders. Fintech Weekly views this as part of BNPL’s shift toward embedded, everyday finance.
Samsung Wallet has also introduced in-store installment capabilities, allowing users to divide purchases made directly at the point of sale, giving users access to BNPL without switching apps or platforms.
A Maturing Model
Buy Now, Pay Later products are moving beyond their original design as short-term checkout options. Initially used to split payments quickly with few barriers, they’re now being shaped by regulatory requirements and changing customer expectations.
Many providers have started to update their services in response. Budgeting tools, links to credit bureaus, and clearer eligibility standards are becoming more common. These changes suggest BNPL is being treated less like a convenience feature and more like a mainstream credit product.
Flexible payments still attract users, but success now depends on how well providers support financial decision-making. Companies that offer transparency, stability, and tools for planning are better positioned to maintain relevance.
BNPL remains a part of the evolving credit landscape. As the sector enters its next chapter, often referred to as BNPL 2.0, the providers most likely to lead will be those that focus on transparency, repayment support, and consumer financial health—shaping credit tools that serve users beyond the point of purchase.
