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FROM THE CEO

Finance Derivative Magazine

As we step into 2026, the global financial landscape stands at a remarkable intersection of innovation, resilience, and transformation. What once were nascent technologies and speculative ideas are now reshaping the foundation of financial markets, investment strategies, and risk management frameworks across the world.

The past year reaffirmed that adaptability is not just a competitive advantage — it is an imperative. Geopolitical realignments, digitization of assets, tightening regulatory frameworks, and the evolving expectations of stakeholders have collectively made agility and strategic foresight vital for every institution, investor, and policymaker.

What to Expect in This Issue

In 2026, Finance Derivative continues its mission to be the definitive voice on critical financial trends — from the maturation of digital assets and decentralized finance (DeFi) to the refinement of quantitative risk models in an era of heightened volatility. Our editorial focus this year is grounded in clarity and depth: we aim to decode complexity and provide actionable insights that support sound decision-making.

Market Evolution: In-depth analysis on how derivatives markets are responding to macroeconomic adjustments, inflationary pressures, and interest rate dynamics.

Technology & Innovation: Thought leadership on artificial intelligence in trading, real-time risk analytics, and the operational implications of blockchain infrastructure.

Regulatory Outlook: Expert perspectives on compliance, cross-border supervision, and the implications of emerging global financial standards.

Sustainable Finance: Coverage on climate-linked derivatives, ESG risk pricing, and how sustainability goals are being integrated into risk and investment frameworks.

As the financial ecosystem evolves, so does our commitment to you — our reader. We are focused on delivering content that bridges theory and practice, challenges conventional wisdom, and sparks constructive dialogue across disciplines and markets.

Thank you for being part of our community. Your engagement and insights continue to shape the discourse that drives this industry forward.

Here’s to a year defined by innovation, integrity, and growth.

Mehtab Chisti CEO

CEO and Publisher

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Banking

11 Rewiring Global Payments: Why Infrastructure, Not Innovation, Is Holding Us Back

By Aaron Holmes, CEO at Kani

28 AI Is Banking On Data Intelligence

By Roman Stanek, CEO, GoodData

38 Bridging the AI gap in banking: From pilot projects to proven performance

By John O’Brien, CEO, NashTech

Technology

20 Game of Drones: The Battle for the Skies

By Varsha N, Assistant Professor, Acharya Institute of Technology

24 The Rise of ‘Finance-asFeature’ in Corporate Tech Stacks By Alex Mifsud, CEO and Co-founder, Weavr.io

42 Sprawling cloud cost is the risk that accountants are overlooking By Paul McAdam, Director, Source Code Control

50 Fintech vs old tech: the UK must create a new vision

By Gianluca Berghella, CEO of Armundia Group

52 The Budget: Tech Sector Needs Clarity and Reform on R&D

By Jake Rickhuss- MD Commercial and Co-Founder of Tech Consultancy Journi

Business

40 How women can drive the future of finance

By Radhika Kapur, VP EMEA Partnerships & Technology, Confluent

46 Lowering business costs by bridging the data divide

By Matt Kent, director of engineering, EMCOR UK

54 Trading on borrowed time: How AI can rescue ageing platforms

By Alexander Goncharuk, Managing Director, UK, and Global Head of FSI at Intellias

58 Toward a Sustainable Plastics Economy: Technological Innovations Reshaping Global Business

By Prof. Dr. André Nijhof, Professor Sustainable Business and Stewardship at Nyenrode

Wealth Management

16 Agentic AI: A New Era for Insurance Transformation?

By Manoj Pant, Senior Director, Strategy and Business Development at Pegasystems"

6 What Investors Should Be Looking Out for in 2026

By Rebecca Sutherland, Investor, Founder, and CEO of HarbarSix

18 Credit risk: the overlooked tool that prevents supply chain failures

By Martin Coufal, Director Partnerships & Business Information, Creditinfo Group"

Finance

14 The future of retail payments: What it means for UK businesses

Alla Gancz, Payments Vertical Leader, Thoughtworks.

22 Responsible AI in Finance: Balancing Innovation with Governance

Dr Supriya Kapoor, Assistant Professor, Trinity College Dublin

30 Woman empowerment in finance

Gillian Whelan MD and Country Manager at international IT & business consultancy emagine’s operation in Ireland.

36 Why Architecture, Not Just Algorithm, Will Define the Future of Financial Services

By Dean Clark, CTO, GFT

44 Why financial inclusion is only half the job

By Jonty Rawlins, Director of Sustainability at Platcorp

48 Financial inclusion begins with infrastructure, not access By Felipe Hillard, Chief Client Network Ofﬁcer, RTGS.global."

Winners List

31 2025 Winners List

What Investors Should Be Looking Out for in 2026

Let’s cut to the chase. If you’re investing in 2026 and you’re not looking at how the world is shifting under your feet, you’re already behind. I’ve seen too many opportunities slip through because people ignored what was coming. But here’s the thing — what’s coming isn’t just about markets or margins. It’s about people.

I talk to investors all the time who are burnt out by the noise — the constant hype, the next big thing, the endless cycle of boom and bust. The truth is, the ones who’ll stand out in 2026 will be those who go back to basics: values, clarity, and the ability to back leaders who truly show up.

So, here’s what I’ll be watching — and what I think every smart investor should keep on their radar.

1. Bet on businesses that know who they are

Every founder can pitch growth, but far fewer can explain what they actually stand for. That’s where I start. Do the leaders know their “why”? Do they make decisions guided by values, not just opportunity?

This isn’t idealism — it’s due diligence. According to EY’s 2024 Global Integrity Report, 84% of consumers expect companies to behave ethically and transparently, while 60% of investors are more likely to back leadership teams with clear ESG principles. (EY Global Integrity Report 2024)

When markets wobble, founders who know their purpose stay grounded. They don’t panic, they don’t pivot out of fear, and they don’t lose their best people. I’ll be looking for companies that are anchored in self-awareness and mission, because values are the best form of resilience.

2. Leadership mindset will make or break growth

We’ve all seen businesses that looked promising on paper and then fell apart because the leadership culture was toxic. It doesn’t matter how brilliant the product is — if the people at the top are running on fumes, the business will eventually follow.

In 2026, the investors worth listening to will be the ones evaluating mindset as much as market share. Deloitte’s 2025 research shows that companies with strong employee wellbeing outperform peers by 11% in productivity and 25% in retention. (Deloitte Insights, 2025)

As someone who mentors founders, I’ve noticed that the most effective leaders aren’t the loudest in the room — they’re the ones who know when to pause, reflect and adapt. They understand that growth isn’t a sprint; it’s a rhythm. And for investors, backing that kind of leadership is one of the smartest decisions you can make.

3. Watch for clarity, not complexity

If a business can’t explain its model in plain English, that’s a red flag. Complexity hides confusion, and confusion kills confidence.

In 2026, investors will need to cut through the noise. Look for transparent reporting, clean governance, and founders who can tell you what they do — and how they make money — without hiding behind jargon.

The OECD forecasts UK GDP growth of around 1.3–1.5% in 2026, which means it’s not a year for blind optimism. Access to capital will be tighter, and only businesses with efficient cash flow and measured plans will stand out. Investors who prioritise clarity over charisma will be the ones sleeping better at night.

4. Relationships will define returns

Behind every deal, there’s a human story. The best returns in my portfolio haven’t come from luck — they’ve come from trust. From partnerships where communication stayed open and everyone understood what success looked like.

A 2024 McKinsey study found that emotional intelligence is now one of the top three factors influencing long-term business success. (McKinsey & Company 2024) It’s not a soft skill; it’s a strategic one. Investors should be looking for founders who balance empathy with execution — the ones who can build culture as well as revenue.

Because money follows meaning, and meaning comes from how you treat people — investors, staff, and customers alike.

5. Keep perspective — slow is still progress

Everyone wants the next unicorn, but patience is underrated. The companies that will outperform in 2026 won’t be the flashiest; they’ll be the ones that grow steadily, manage resources wisely and prioritise long-term value over shortterm hype.

There’s power in showing up consistently — through setbacks, pivots, and plateaus. As investors, our job isn’t just to spot opportunity; it’s to recognise resilience.

The bottom line

If you want to invest well in 2026, stop chasing noise and start reading people. Numbers matter, but integrity matters more.

Back leaders who are values-driven, emotionally intelligent, and clear on what they stand for. Those are the businesses that won’t just weather uncertainty — they’ll shape what comes next.

Because in a market obsessed with speed, the real edge lies in clarity, connection, and conviction.

Rebecca Sutherland is the visionary force behind HarbarSix, a hybrid investment fund and business accelerator designed to power up high-potential founders with more than just capital. At the heart of her mission is a belief that exceptional businesses are built not only with smart strategy but with empowered leaders and the right ecosystem of support.

With over 20 years of experience in scaling small businesses and transforming overlooked ventures into sustainable success stories, Rebecca brings a unique blend of commercial acumen, leadership insight, and emotional intelligence to the table. She has a sharp eye for spotting potential where others see obstacles, and she’s on a mission to make sure bold ideas don’t fall through the cracks simply because they don’t ﬁt the traditional startup mould.

Through HarbarSix, Rebecca leads a highly selective programme investing in six standout businesses every six months. But this isn’t your average accelerator. HarbarSix offers deep partnership, one-on-one coaching, access to expert networks, and a shared toolkit that founders can use. It’s a growth ecosystem built for those who are ready to do the work and scale with integrity.

Rebecca’s approach is grounded in the belief that mindset drives results. She champions founders who lead from within, and she’s known for combining big-picture strategy with the kind of practical, hands-on support that truly moves the needle. Whether guiding a business through a make-or-break quarter or helping a founder breathe through a boardroom curveball, her leadership is clear, calm and unapologetically committed.

At HarbarSix, Rebecca isn’t just investing in businesses; she’s backing people, because she knows that when founders grow, their companies follow.

https://www.harbarsix.com/

Rebecca Sutherland, CEO & Founder, HarbarSix

Rebecca Sutherland

Rewiring Global Payments:

Why Infrastructure, Not Innovation, Is Holding Us Back

Trillions move across borders every day—yet global payments remain riddled with hidden friction. APIs, wallets, and real-time rails promise instant, borderless finance. But beneath the surface, disconnected systems, outdated data flows, and legacy processes create delays, costs, and complexity that innovation alone can’t solve.

This isn’t a technology gap. It’s an infrastructure problem. Solving it will require more than faster front ends or shiny new schemes. It calls for a fundamental rethink of how the industry handles data, processes, and reconciliation at scale.

The Hidden Cost of Fragmentation

Every cross-border payment still passes through a gauntlet of intermediaries—card

schemes, processors, correspondent banks, FX handlers, local settlement systems—each introducing its own data format, reference logic, and timing mismatches. Even for regulated, compliant actors, the cost of moving money remains unnecessarily high—not because of fraud or FX spreads, but because of basic operational inefficiency.

Aligning all that information is difficult and prone to error. When data doesn’t line up, financial teams resort to manual workarounds—delaying settlements, masking revenue leakage, and risking non-compliance.

According to the World Bank, the average global cost of sending remittances is still over 6%—twice the G20’s 2030 target. But that’s only the visible cost. Beneath the surface lies the operational drag:

hours spent stitching together inconsistent data, reconciling across platforms, and managing regulatory risk manually. The true cost of fragmentation is paid in time, not just fees.

Instant Payments, Delayed Operations

Domestic real-time payment systems— like SEPA Instant, FedNow, and Faster Payments—are speeding up local transfers. But in the cross-border world, settlement still hits friction the moment a transaction crosses regulatory or institutional boundaries.

Most institutions still rely on overnight batch files, fragile CSV exports, or manual data mapping to reconcile flows and trigger downstream reporting. This creates a systemic brake on the promise of real-time finance.

The faster the front end moves, the more pressure it puts on the middle and back

office. And without the right infrastructure, speed becomes a liability, not an advantage.

The Fix Is Shared Infrastructure, Not More Platforms

Fixing fragmentation doesn’t mean launching another platform. It means rewiring how global payments infrastructure actually connects—how systems talk, how data is standardised, and how value moves across jurisdictions.

That’s where initiatives like ISO 20022 matter. It offers a gateway to richer, more structured transaction data that can be parsed, validated, and actioned automatically. But ISO compliance alone means nothing if businesses don’t build the infrastructure to ingest and interpret that data in real time.

Likewise, cloud-native architectures and modern API integrations are powerful only when used to connect systems

meaningfully—not just expose endpoints. Interoperability needs to start with shared logic: how transactions are tracked, matched, and reported across systems. Without that, every connection is another bespoke build.

Smarter Doesn’t Mean More Complex

What’s needed now isn’t another layer. It’s fewer. Smarter infrastructure reduces complexity by standardising data, automating matching logic, and eliminating handoffs. It gives operations teams confidence in the accuracy of their numbers, but without having to check three portals and an Excel macro to confirm it.

That shift is already underway. Banks, fintechs, and payment institutions are realising that sustainable growth doesn’t come from bolting on new features. It comes from building systems that handle complexity for you—quietly, in the background.

Collaboration Is the Bottleneck

Global payments can’t evolve without shared standards. New systems are emerging—BIS Nexus, SWIFT gpi, domestic real-time rails—but without common ground on how data is structured and exchanged, scale remains out of reach.

It’s not the pipes that are missing, it’s the protocols. Every process, every fraud check, every scheme report, every sign off, relies on clean, interoperable data. Until those foundations are aligned, even the best technology will be forced to work around the problem rather than solve it.

Beyond Efficiency: A Global Opportunity

When global payments become easier to trust and track, new forms of commerce

unlock. A marketplace seller in Ghana can offer refunds as seamlessly as a retailer in Manchester. A freelancer in India gets paid on delivery, not after a week of processing delays. A CFO in Berlin can reconcile scheme fees the same day, not the same quarter.

These aren’t edge cases. They’re the future of financial inclusion, digital commerce, and cross-border trade. And they won’t be powered by another sleek app. They’ll be powered by infrastructure that just works.

Whether funds are moving between neighbouring markets or across continents, reliability and transparency are what unlock trust—and infrastructure is what determines both.

Infrastructure as Competitive Edge

Global payments need a single source of truth: clear visibility, predictable

settlement, and immediate awareness when something deviates from plan. The industry’s next leap forward depends on systems that deliver that clarity automatically, without human intervention or delay.

Modern infrastructure must support scale and certainty in equal measure. When data moves seamlessly between banks, schemes, and processors, operations stop being reactive and start becoming intelligent.

The real advantage now lies in orchestration—making the entire payments ecosystem operate as one coordinated system, where data alignment replaces manual effort and accuracy becomes the default state.

Aaron Holmes, CEO at Kani

The future of retail payments: What it means for UK businesses

A collaborative approach to execution and delivery

Alla Gancz, Payments Vertical Leader, Thoughtworks.

The UK is gearing up for one of the biggest overhauls to its payment systems in decades. In November, the Payments Vision Delivery Committee (PVDC) released a unified plan and roadmap to modernise how retail payments work across the country. The plan builds on earlier announcements about a new retail payments framework designed to make transactions faster, safer, and more efficient. In short, the UK is reimagining how payments should work in a modern economy.

The new model brings together government bodies and industry partners to work in a more coordinated way. At the centre of this model is the Retail Payments Infrastructure Board, chaired by the Bank of England. This board’s job is to take big-picture goals and turn them into real, workable technical plans, making sure policy ideas actually translate into practical change. It will include a broad mix of voices, from banks and fintechs to merchants and consumer groups.

Supporting this is an industry-led delivery company that will manage procurement and oversee implementation. This division of responsibilities addresses a long-standing challenge in infrastructure projects: balancing operational stability with innovation.

By separating day-to-day operations from development, the UK can experiment boldly without risking resilience or efficiency.

Understanding the Five strategic outcomes for the future of retail payments

The PVDC has set out five strategic outcomes that guide how the next generation of retail payments should be designed. Together, they turn the Vision’s pillars of innovation, competition and security into practical goals that increase choice, resilience and trust across the financial system.

At their core, these outcomes aim to give consumers and businesses more

choice. Payments should be affordable and data-rich supporting everything from account-to-account payments at the till to automated and programmable services.They also focus on making different forms of money work smoothly together, whether using bank money, tablecoins or even future CBDCs.

Trust will be built in from the start with a dual focus on strong fraud prevention and open payment infrastructures, supported by regulation and effective governance.

Finally, the system is designed to be resilient at scale. Payments are always secure and well governed, with settlement in central bank money and oversight by national authorities to support long-term confidence.

Embracing new forms of digital money

An exciting aspect of the new framework is its focus on integrating digital money into mainstream payments. Alongside a potential digital pound, the infrastructure is being designed for frictionless transfers across stablecoins and tokenized deposits.

A payments landscape in which different forms of money can coexist and interoperate seamlessly, is the goal. Achieving this will require new “core interoperability” frameworks capable of connecting traditional and digital systems securely and efficiently. This approach not only enables frictionless transfers between new and existing forms of money but also lays the groundwork for a more inclusive and competitive digital economy.

Technology as the foundation

The success of this transformation depends on technology. The new infrastructure will be cloud-native, moving away from the mainframe systems that have dominated for decades. Cloud platforms offer scalability and cost efficiency, as well as the ability to roll out new features quickly.

Open APIs will replace proprietary interfaces, allowing authorised participants to build services on top of the core infrastructure. Built-in fraud checks and digital identity verification will keep payments secure without adding friction, while tokenisation will allow different forms of money to work together within

one system, supporting flexibility.

Opportunities and challenges

For banks, the transition will be multifaceted and transformational. While legacy systems may slow progress, deep customer relationships and regulatory expertise remain key strengths. Success will depend on aligning technology strategies with the Payments Forward Plan and engaging actively in governance.

Fintech firms are well positioned to benefit. They will have a formal role in shaping national payment infrastructure, with their innovation and user-centric design giving them an edge in programmable money and digital assets.

Looking forward and future plans

This announcement marks only the beginning. The UK now moves into a phase focused on broadening choice, connecting a multi-money ecosystem, embedding fraud protection into the infrastructure, and ensuring open, resilient access for every participant.

If the UK can deliver on this, it will set a global benchmark for open, trusted and innovation-ready payments. The next twelve months will be pivotal in turning these outcomes into tangible benefits for consumers, businesses and the wider economy.

Agentic AI: A New Era for Insurance Transformation?

Manoj Pant, Senior Director, Strategy and Business Development at Pegasystems

Many are heralding agentic AI as the standout technology to come out of 2025, with Gartner predicting that by 2028, it will enable 15% of day-to-day work decisions to be made autonomously.

With AI already transforming many aspects of the insurance industry from improving customer interactions to automating underwriting activity, we’ll soon be entering insurance’s agentic age, moving beyond automation and redefining how the industry operates. With this, what are the main considerations that need to be factored in for this next era to be truly successful? Let’s take a look.

The potential of agentic AI

Agentic AI refers to autonomous agents capable of automating complex tasks with minimal to no human supervision.This enables insurers to eliminate routine, high-volume administrative duties, as these agents work tirelessly, scaling operations 24/7 and potentially delivering more accurate outcomes at a lower cost.

Agents can also transform underwriting by accelerating risk assessment

and policy approvals, alongside automating claims processing and settlement, delivering real-time empathetic customer service in line with rising consumer expectations, and proactively detecting fraud.

Agentic AI also supports regulatory compliance, such as automated monitoring under frameworks like Consumer Duty. Early agentic AI adoption offers a competitive edge enabling personalised offers, human-like AI conversations, and on-demand products.

Inevitable challenges

As with all new technologies, agentic AI adoption comes with some challenges, with insurers’ increasing reliance on such technologies raising some critical concerns.

One key issue is systemic risk, as financial regulators have warned about the dangers of core decisions becoming too dependent on AI systems. Over-automation can lead to a loss of human expertise, gradually eroding the institutional knowledge and judgment that remain essential for handling complex insurance scenarios. Operational vulnerability

becomes a real threat when firms create single points of failure by relying too heavily on AI, potentially leading to major disruptions if systems malfunction or deliver unexpected outcomes.

Decision transparency may suffer, as excessive reliance on AI can make it challenging to explain decisions to customers or meet regulatory accountability expectations. Insurers must strike a careful balance of leveraging AI for efficiency and innovation while maintaining the human oversight essential to the industry.

Another hurdle comes with legacy systems, as fragmented, poor-quality data can make it difficult to train reliable AI models. Some critical information remains unstructured and historical insurance data (e.g., handwritten claims, voice records) is hard to process without significant pre-cleaning. Having clean, integrated data, supported by strong governance and compliance frameworks is critical for AI success.

Futureproofing business operations and the workforce

Investing in legacy platforms is crucial to agentic AI success, with modern infrastructure and cloud-native platforms enabling rapid deployment and iteration of AI models. Processes must be updated end-to-end, with insurers evaluating their full technology stack to realise value from this type of AI - it’s not enough to simply layer AI on top of existing systems or add unnecessary tools into workflows. The few insurers who’ve succeeded have done so by treating AI as a business transformation initiative, not just a technology upgrade.

Insurance is a highly regulated industry, so it’s important that agentic AI follows clear rules and processes. Everything should be auditable and insurers should be able to see how various decisions were made. By building controls into each agent, insurers can keep tabs on their behaviour and make sure they’re being used responsibly.

Employee development is also crucial as advanced AI is integrated across a business, as we can’t expect the best from our teams if we are not providing them with the right tools. The recent Mind The Gap report noted 69% of organisations report a lack of available skills is preventing them from adopting AI solutions, further proving we should be investing properly in our people. Employees may resist adopting AI tools without proper training or if they feel it threatens their roles, so we need to see AI and intelligent automation not as a threat or a complicator but as a way to elevate employees' experience and improve their productivity.

Overall, the insurance industry is evolving rapidly, and agentic AI has the potential to reshape how work gets done. It is already improving the overall productivity of insurers, meeting rising customer expectations and improving employee experiences. To unlock the full value of agentic AI, insurers must continue to invest in modern technology infrastructure, robust governance frameworks, and workforce training. While AI has already made a significant impact, agentic AI represents the next leap and its true potential is only beginning to emerge as we head into 2026.

Martin Coufal, Director Partnerships & Business Information, Creditinfo Group

rating payment patterns, mounting debt, liquidity problems. But most companies weren't looking. A single supplier default can trigger problems that ripple through delivery schedules, damage reputations and create significant financial losses. By integrating credit risk evaluation

outstanding obligations and credit score into an accessible profile, providing an objective creditworthiness snapshot.

Coca-Cola integrates credit data into its supplier evaluation process. This ensures it builds new partnerships on

stable financial foundations, reducing disruptions and maintaining a consistent supply of critical raw materials.

But credit scores alone don't tell the whole story. Many organisations enhance their analysis by conducting comprehensive due diligence that combines financial data with operational, geopolitical and reputational factors.

For example, Siemens conducts detailed financial risk analyses as part of its supplier onboarding and monitoring process, examining balance sheets, liquidity ratios and debt structures as well as qualitative factors such as regulatory compliance and industry-specific risks. This comprehensive approach enables companies to uncover hidden vulnerabilities that might put supply chains at risk.

Real-time visibility into risk

Companies need to establish risk assessment as an ongoing process. Supplier financial health can deteriorate rapidly due to external and internal factors. Forward-thinking companies adopt continuous monitoring systems that give them real-time tracking of late payments,

bankruptcy filings, credit downgrades, or sudden liquidity declines.

Ford Motor Company, for example, uses real-time monitoring to identify early warning signs of supplier distress to minimise disruptions and protect production schedules.

The technology advantage

The growing adoption of digital tools has significantly enhanced credit risk assessment. AI, machine learning and predictive analytics are increasingly used to forecast potential supplier distress before it becomes visible in traditional credit reports. These tools analyse not only financial statements but also alternative data sources such as trade flows, market sentiment and macroeconomic indicators.

Critically, the democratisation of credit risk technology means smaller businesses can now access simplified versions of these tools to protect themselves against supplier failures, strengthening supply chains and levelling the playing field with global giants.

Resilience through better data

In a global economy marked by volatility and uncertainty, credit risk evaluation has evolved from a compliance exercise into a strategic operational advantage. Organisations that incorporate financial data, comprehensive due diligence, real-time monitoring systems and emerging technologies can dramatically reduce vulnerabilities and build stronger supplier relationships.

In an environment where supplier failures cost businesses $184 billion annually, the companies that survive and thrive will be the ones treating credit risk as seriously as they treat product quality or delivery times.

Game o� D�ones: The Battle �or the Skies

Varsha N, Assistant Professor, Dept. of Aeronautical Engineering, Acharya Institute of Technology.

The rapid rise of drone technology has ignited a new era in aviation - one that is fundamentally redefining control of the skies. Once confined to limited military reconnaissance roles, drones, formally known as Unmanned Aerial Vehicles (UAVs), have evolved into powerful systems used across civilian, commercial, and defense sectors. This evolution has given rise to a global “battle for the skies,” where technological innovation, security needs, regulation, and ethical considerations compete for dominance.

Advancements in technology have been the driving force behind the widespread adoption of drones. Modern UAVs are equipped with GPS navigation, advanced sensors, artificial intelligence, and

autonomous flight capabilities. These features allow drones to perform complex missions such as real-time surveillance, precision mapping, automated delivery, and coordinated operations. As drones become smarter, smaller, and more affordable, their presence in shared airspace continues to expand rapidly.

In the civilian and commercial domains, drones are reshaping traditional industries. In agriculture, they enable precision farming by monitoring crop health, optimizing irrigation, and improving yield efficiency. The construction and infrastructure sectors use drones for surveying, site inspections, and progress monitoring, significantly reducing cost, time, and human risk. Logistics companies are experimenting with drone-based delivery

systems to provide faster and more efficient transportation of goods, especially in remote and congested urban regions.

The battle for the skies is most intense in the field of warfare and defense. Modern warfare is undergoing a dramatic transformation, with drones emerging as decisive battlefield assets. Military UAVs are widely used for intelligence, surveillance, and reconnaissance (ISR) missions. Equipped with high-resolution cameras, infrared sensors, and radar systems, drones can monitor enemy movements in real time without exposing pilots or ground troops to danger. Their ability to loiter for long durations at relatively low cost provides commanders with persistent situational awareness and faster decision-making capabilities.

Beyond surveillance, drones play a crucial role in precision strike operations. Armed UAVs can carry guided missiles and smart munitions to accurately engage high-value targets while minimizing collateral damage. This capability has made drones indispensable in counter-terrorism missions and asymmetric warfare, where speed, accuracy, and reduced human risk are critical.

A major technological leap in modern warfare is the development of swarm drones. These systems consist of multiple UAVs operating collaboratively using artificial intelligence. Drone swarms can overwhelm enemy air defenses, conduct coordinated surveillance, disrupt communication systems, or execute electronic warfare missions. Their low cost,

redundancy, and adaptability give them a strategic advantage over traditional defense systems.

Drones also support battlefield logistics and post-mission assessment. They are used to deliver medical supplies, ammunition, and essential equipment to frontline troops in hostile or inaccessible areas. Additionally, drones assist in damage assessment after attacks, enabling rapid evaluation of mission effectiveness.

Despite their many advantages, the increasing use of drones presents serious challenges. Airspace congestion, safety risks, privacy violations, cybersecurity threats, and ethical concerns—especially regarding autonomous weapon systems—have become critical issues.

Unauthorized or hostile drone operations pose risks to civilian aviation and national security. Consequently, governments and defense organizations worldwide are investing not only in drone development but also in counter-drone technologies and regulatory frameworks.

In conclusion, the “Game of Drones” represents a global contest between technological advancement and responsible governance. As drones continue to dominate the skies, the true challenge lies in balancing innovation with safety, security, and ethical responsibility. The outcome of this battle will shape the future of aviation, warfare, and global security in the decades to come.

Responsible AI in Finance: Balancing Innovation with Governance

Artificial Intelligence (AI) is having a profound and multifaceted impact on the finance sector, redefining how risk is modelled, compliance is managed, and investment decisions are made. Its rapid adoption also brings new considerations around explainability, bias, governance, workforce evolution, and regulatory oversight.

The financial industry has enthusiastically embraced AI to address chronic pain points and capitalize on new analytical opportunities. Algorithmic advances and the availability of vast data streams allow institutions to predict and mitigate risks, automate compliance, and generate high-value investment insights with unprecedented efficiency. AI’s promise is

not just speed, but an ability to uncover patterns too complex or subtle for traditional approaches.

Risk-modelling, compliance and investment

AI’s integration into risk modelling enables financial institutions to move beyond static, backward-looking assessments. Machine learning models ingest high-volume, real-time market, credit, and transaction data, flagging anomalies and predicting adverse market movements with accuracy unattainable in conventional frameworks. Deep learning approaches can now assess creditworthiness by processing both traditional financial metrics and alternative data, such as social signals and spending behaviour.

In compliance, AI-driven automation has revolutionized how regulatory requirements are identified, interpreted, and adhered to. Natural language processing systems sift through thousands of regulatory updates and flag relevant changes, dramatically reducing manual workloads. AI also enhances anti-money laundering protocols by rapidly analysing transaction networks, detecting fraud and suspicious behaviour. A well-known example is J.P. Morgan’s COIN (Contract Intelligence) platform that uses natural language processing to analyse 12,000 loan agreements in seconds, saving over 360,000 hours of manual compliance review annually. This dramatically streamlines regulatory document analysis and reduces operational costs.

Investment decision-making is increasingly reliant on AI to analyse market sentiment, optimize asset allocations, and perform scenario-based portfolio projections. Generative AI synthesizes market data, economic indicators, and global news to help managers act quickly and confidently. For example, BlackRock’s Aladdin platform leverages machine learning to dynamically manage risk and portfolio strategies, reportedly increasing returns and enabling real-time responses to market changes. With predictive analytics, investors can exploit statistical signals in large time-series datasets that would otherwise go unnoticed.

Challenges

With increased reliance on AI, finance professionals, clients, and regulators face heightened challenges around model explainability. Many high-performing AI models function as “black boxes,” yielding forecasts without clear logic trails. In finance, where regulatory scrutiny and stakeholder trust are paramount, this lack of transparency can undermine decision legitimacy and trigger reputational damage.

Bias is another persistent concern. If training data reflects historical prejudices or unbalanced sample groups, predictive models may perpetuate these inequities in credit, insurance, or investment decisions. This puts institutions at risk for ethical breaches and regulatory investigation.

Governance frameworks are essential for responsible AI deployment. Institutions are devising multi-layered review processes that include regular audits, performance verifications, and cross-functional oversight. Regulators, via entities such as the EU AI Act, are demanding pre-approval, transparency, and robust bias mitigation before AI tools are adopted at scale. Continuous improvement,

stakeholder education, and interdisciplinary engagement are now essential to maintain market integrity and public confidence.

The workforce implications of AI in finance are complex. While automation displaces some repetitive, rule-based functions, it also generates demand for “hybrid” roles that combine financial expertise with AI literacy and governance competency. Analysts, compliance officers, and IT strategists are increasingly required to interpret algorithmic outcomes, monitor model fairness, and bridge communication between technical teams and business leadership.

Regulatory oversight is intensifying as national and international bodies seek to keep pace with innovation. Financial authorities are updating compliance codes, conducting impact assessments, and fostering collaborative dialogue between AI firms and market participants. The evolving regulatory landscape mandates not only technological sophistication but also ethical and procedural rigor to safeguard systemic stability.

Artificial intelligence is not simply enhancing financial processes, it is catalysing structural transformation across risk management, compliance, and investment functions. The sector’s challenge is to balance AI-enabled innovation with stringent standards for transparency, fairness, and governance.

To harness AI’s full potential while safeguarding stakeholder interests, institutions must invest continually in robust frameworks for model oversight, interdisciplinary skill development, and proactive regulation. The future of finance will turn not on the breadth of AI’s adoption, but on the depth of its responsible application, where ethical, explainable, and inclusive technology drives both business success and public trust.

Dr Supriya Kapoor, Assistant Professor, Trinity College Dublin

Research from our recent survey shows that 92% of SaaS platforms expect to have embedded financial capabilities live by the end of 2025, up from 74% a year earlier. The implications are profound: financial services are no longer being sold to businesses as discrete capabilities. They are being distributed through the software businesses rely on most.

Welcome to the era of ﬁnance-as-feature - where business applications and financial services no longer exist as distinct parallel capabilities, but fuse into a single, seamless solution to a business problem domain, such as managing suppliers, or retaining employees. In this

new paradigm, software does more than orchestrate workflows; it becomes the conduit for money itself in the context of delivering an outcome. The boundaries between “doing business” and “doing finance” blur: issuing a spendable card, automating vendor payments, or reconciling expenses become capabilities built directly into the systems companies use to run their day. Finance stops being something you bolt on; it becomes part of what you build.

From using finance to creating it

Software platforms that once relied on third-party payment providers are now the point of access for financial activity themselves.

Finance as infrastructure, not integration

Embedded finance has matured from an integration model into an infrastructure layer that sits comfortably within software products. There is enough maturity in embedded finance platforms now - rich webhook libraries, biometric-based authentication SDKs, embeddable mobile wallet provisioning tools, to name a few - for SaaS product and engineering teams to consider financial services as an enhancement technology like AI, and a highly accessible one at that.

By partnering with regulated, licensed providers, SaaS platforms can safely and quickly offer compliant financial capabilities, without the operational or regulatory burden of becoming financial institutions themselves.

They’ve turned financial functionality from a cost into a source of value. Historically, adding payments or fund flows was seen as overhead, a compliance-heavy cost centre. Now, every transaction, every spendable card, every held balance can generate new revenue or data advantage.

A good example to consider is Finway. Finway is a German financial operations platform which integrates cards, payments and expense controls directly into its product. This allows mid-sized companies to automate purchasing and budget management in one workflow, removing the need for separate financial portal and manual reconciliation. Finway makes two times the revenue per user compared to non-finance users. Financial capability has become a profit driver, not a liability.

That transformation is only possible because certain embedded finance platforms hold or partner with licensed entities such as Electronic Money Institutions (EMIs). Possessing a regulatory licence is increasingly viewed as a stamp of trust from the regulator; proof the model can scale safely, with compliance and risk controls embedded at its core.

A new model for distribution

For financial institutions, this evolution should be seen not as a loss of visibility but as a gain in reach.

Serving small and medium-sized businesses directly has long been uneconomical; margins are thin, onboarding costly, and expectations for digital experience high (McKinsey). SaaS platforms already understand these businesses intimately. They manage their payroll, invoices, subscriptions, and procurement. They hold the data, context, and engagement that traditional financial institutions often lack.

By selling and delivering financial services through SaaS platforms, everyone benefits. The end customer gets simplicity and speed. The financial institution gains efficient access to a market it could not profitably reach and serve alone.

This isn’t disintermediation – it’s evolution. The interface changes, but the value chain becomes stronger.

Why this model succeeds where BaaS faltered

During the early wave of Banking-as-a-Service (BaaS), many partnerships were assembled through multiple intermediary layers that connected financial institutions, fintechs, and platforms in complex chains. When oversight slipped, fragility followed.

In 2023, several financial institutions offering BaaS were subject to regulatory enforcement actions for lapses in partner oversight, forcing some to pause fintech onboarding while they strengthened controls.

The lesson isn’t that BaaS was wrong; it’s that what needs to go on top matters, and secondly, it’s increasingly complex and expensive to build and run to the standards expected by regulators. Embedded finance frameworks

designed around a comprehensive stack, which encompasses licensed financial entities, robust processing capabilities, comprehensive and modern compliance technology, is proving to be more resilient.

The

valuation premium of finance-native SaaS

The financial markets are starting to price this shift in.

According to William Blair, SaaS platforms implementing embedded finance models demonstrate superior growth, retention, and monetisation, which together drive valuation premiums over peers. Similarly, the Worldpay Merchant Payment Experience Report 2025 found that embedded payments revenue is valued at 8–12× ARR, compared with 4–6× for standard SaaS subscription revenue.

In short, markets already reward the integration of finance into software. Just as cloud-native companies once commanded higher valuations than their on-premise predecessors, the next generation of finance-native SaaS platforms will be valued for their ability to capture, move, and manage money within their ecosystems.

From standalone finance to built-in finance

As we move into 2026, the story of embedded finance is no longer about exploration. It’s about execution, real customers, and measurable impact.

Finance is no longer something businesses consume. It’s something they build into what they do.

And for those who enable it responsibly – SaaS builders, licensed intermediaries, and forward-thinking financial institutions – it’s an opportunity to redefine how financial services are delivered, experienced, and valued.

Alex Mifsud, CEO and Co-founder, Weavr.io

AI Is Banking On Data Intelligence

- Roman Stanek, CEO, GoodData

Banks have almost entirely moved from paper and branches to smartphones and online platforms. Just as this new normal goes global, with neobanks challenging the establishment, the banking sector is on the verge of its biggest technology shift since digitisation. Welcome to AI as usual.

Technology has always powered banking. Today, banking sector leadership is no longer just about operational effciency, It’s about enabling intelligence. AI offers a

new infrastructure of insight, speed and competitive advantage for those who can adopt it at speed.

ChatGPT was the fastest adoption of new tech the world has seen. Banks no longer ask if they’ll use AI, but how AI can help shift strategies faster, serve savvy customers in smarter ways, and better govern and manage risk. Doing so involves Data Intelligence, a move beyond static reporting and legacy architectures toward a new model of intelligent banking. Just when they thought they were done, it's time to get moving again, at pace.

A threefold challenge

The fnancial world operates on the most complex, highly regulated, and data-intensive systems, or rails, ever created. Just as the industry overcame the frst major hurdle of modernising legacy infrastructure, they now have to create AI-driven architectures to satisfy a more demanding second challenge: meeting rising customer expectations.

Today’s customers expect instant account onboarding, personalised spending and investment insights, and, of course, 24/7 customer service. They place deep trust in the transparency and auditability of banking systems confdent they comply with strict regulatory standards. They want this reassurance even when dealing with brand new fnancial instruments like crypto currencies.

However, it's not only banks who are modernising. In the background, malicious actors are evolving to use AI as an attack vector, making cybercrimes faster and more devastating than ever. Fraud is the third major challenge for banks. This means banks need to adopt LLMs with extreme care.

The result of this complex AI cocktail of pressures? An industry caught between innovation and stagnation. Banks recognise the urgent need to modernise to compete with neobanks and other unconventional challengers, but they cannot afford to compromise on traditional controls or compliance.

The forever-changing compliance landscape

Regulation has always defned banking. It’s the framework that keeps the system fair, transparent, and trustworthy. But today, regulatory frameworks change faster than ever before. Banks face relentless challenges and a growing number of regulators demanding precise, real-time reports on an ever-expanding body of global standards.

This was slow, costly, and human and therefore, often error-prone work. But now that’s all starting to change.

AI is redefning what compliance can and should be. Instead of armies of analysts manually verifying every record, AI-driven systems cluster and validate data automatically, cutting through complexity with speed,

accuracy, and transparency. The result isn’t just faster reporting. It’s stronger compliance readiness, building on a foundation of trust and clarity for customers.

But there’s a deeper shift happening beneath the surface. As technology becomes more capable, regulators themselves are forced to evolve. The rules that governed the last century’s fnancial systems won’t be enough for the next one. Frameworks will need to adapt, and the very structure of global fnancial markets will change with them.

From dashboards to decisions

For years, fnancial analytics was a story of dashboards and data; confusing and with a potential for obfuscation. Too many metrics, too little meaning. But that era is at an end.

AI-native intelligence is transforming reporting from static to dynamic, turning data into real-time insight. Financial teams can now interact with AI copilots that detect anomalies, predict risks, and recommend actions in real-time. It’s not just automation happening. This is transformation.

The future of banking belongs to institutions who are brave enough to own their evolution. They will deploy their own LLMs, brand their own copilots, and modernise at their own, very rapid pace, allowing banks to innovate confdently while preserving trust, compliance, and customer experience.

AI-native institutions: The future of banking

AI isn’t a new wave of banking, it’s a reset. Using the right technology, fraud investigations that once took days can now happen in seconds. Regulatory reports compile and verify themselves in real time. Advisors can deliver personalised fnancial guidance instantly, informed by secure, AI-powered insights.

Those who embrace AI-native data intelligence will move from reporting performance to predicting outcomes. From reacting to risk to preventing it. The future of banking won’t be built on dashboards, data warehouses, or compliance checklists. It will be built on composable data intelligence, underpinned with AI.

Woman empowerment in ﬁnance

Whilst socioeconomic, political and technology challenges continue to unfurl and drive the global financial community, it is important not to lose sight of other priorities on the industry’s agenda. For example, monitoring the progress of female representation in the sector’s top positions.

Building more diverse senior leadership teams – and the benefits research has shown this brings - should be a well-established focus for businesses. Furthermore, pockets of industry data suggest progress is being made, but where are the holdups?

Globally, the picture varies substantially with pressure in the US from President Trump’s roll back on DEI initiatives in January 2025. In the UK in March 2025, the FCA and PRA shelved proposals for mandatory D&I reporting for financial services firms and reports suggest that the gender pay gap remains doggedly wide within the sector.

A look at the figures

25% to 50% female representation.

Interestingly, among the Charter’s SME signatories, there are already higher levels of female representation, on average 51%, and these businesses also set much higher targets with almost three-quarters (73%) committing to reaching 40% female representation in senior management.

Looking purely at female representation in leadership positions, the data tells different stories. For example, a survey in September suggested that less than 20% of senior finance roles in the UK are held by women, and only 9% of CEOs.

In Ireland, recent gender balance data from the Central Statistics Office suggests the finance and insurance sector is also moving in the right direction. In 2025, 38.5% of senior executives in this sector were women, up from 33.9% in 2023, which is notably higher than the average across all business sectors (32.3%).

The trend shrinks and slows at director level, with women making up 32.7% of directors in Ireland’s finance and insurance sector in 2025, having only risen from 31.2% two years ago.

What can women in finance do?

In contrast, the HM Treasury Women in Finance Charter, launched in 2016, suggests much greater progress is being made. According to the latest review, of 205 signatories with more than 250 staff, female representation increased to 36% on average in 2024, up from 35% the year before. In addition, four-fifths have met their targets or are on track to meet them. However, targets are set by individual firms and range from hitting

from seeing opportunities right in front them. But, by actively reframing that mindset into a positive ‘I can’ attitude, women can help to shatter their own glass ceilings.

It takes time and effort to rewire one’s mindset, but with persistence it can be done. There are numerous actions, exercises, and resources, starting with appraising one's own achievements. One step that can be highly beneficial is asking people for feedback on one’s work or way of working. Whilst intimidating, the result can in fact be confidence-building and anything negative is valuable information to absorb and grow from.

The power of advocacy

Women undoubtedly continue to face barriers to the top jobs and unique challenges in finance careers, particularly in more traditional pockets of the sector. Breaking these down is the responsibility of many stakeholders, including HR teams, business leaders, and mentors, among others such as regulators.

Alongside ongoing sector initiatives, to drive change more quickly, women also need to work on themselves. Too often women hold themselves back through a lack of self-belief and fearing failure. A person’s mindset can be their worst enemy if they are constantly questioning their abilities and avoid taking risks. It can prevent talented professionals

Another powerful tool is seeking out advocacy. Women should have the confidence to ask colleagues or contacts with influence to advocate for them, to help push them forwards.

It is of course also vital that these ‘people of influence’ look out for opportunities to advocate for talented women rather than wait to be asked. Advocacy goes beyond mentorship or guidance, it means actively championing women, pushing for their inclusion in leadership development programs, and vouching for their abilities.

Leaders have a responsibility to build confidence, encourage ambition, and create clear pathways to leadership for women. True change happens when leaders take bold steps to ensure women are not just present in senior leadership teams but thriving at every level. Securing greater female representation up the pipeline will eventually lead to more women in the top chair and bring greater balance and perspective to the financial sector.

Gillian Whelan MD and Country Manager at international IT & business consultancy emagine’s operation in Ireland.

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Why Architecture, Not Just Algorithm, Will Define the Future of Financial Services

Across the industry, banks are under pressure to prove that their investments in artificial intelligence (AI) can deliver meaningful results. The technology itself is rarely the problem; the real challenge lies in making it work within the complexity of financial institutions that were never designed for it. Decades of legacy systems, fragmented data and rigid processes make it difficult to run AI reliably, securely and at scale. Therefore, to turn prototypes into production, banks need to modernise the foundations beneath them.

The production reality gap

There’s a pattern emerging across the industry that should concern every financial institution pursuing an AI strategy. A bank develops an impressive machine learning model for credit

risk assessment or fraud detection that performs beautifully in testing phases. When it’s time to deploy it into production systems that process millions of transactions daily, integrate with legacy banking systems,

comply with stringent regulatory requirements and demand near-perfect reliability, the once-promising model often becomes another costly experiment, primarily due to factors beyond the algorithm’s quality.

Most banks are trying to layer cutting-edge AI onto infrastructure that was never designed to support it. Experimentation environments are often isolated, flexible and unconstrained which is the opposite of what’s required in production conditions in financial services.

Financial services operate under constraints that don’t exist in most other industries. A trading system that experiences latency during market volatility can cost hundreds of millions. Similarly, a fraud detection system going offline, even briefly, opens a window for sophisticated attacks that can compromise customer trust and cause irreparable reputational damage.

The ability to operationalise AI reliably and securely, therefore, is less about the model and more about the infrastructure on which the model is deployed.

What

Bank-Grade AI actually requires

Reliable AI in regulated environments demands infrastructure most banks lack. They must begin with scalable compute and storage before tackling more complex needs.

Real-time processing, for one, is non-negotiable as customer expectations are shaped by how quickly they see results. A lending decision that takes three days to process now competes against the instant gratification customers experience everywhere else in their digital lives. The architecture needs to be able to ingest data, run complex models and deliver decisions in milliseconds rather than minutes.

Data governance also becomes more

complex when AI needs access to information that has historically lived in isolated silos across the organisation. An advanced anti-money laundering model might need to analyse transaction patterns, customer communications, third-party data and historical case outcomes simultaneously. Bringing these data sources together securely in a way that satisfies regulators demands infrastructure specifically designed for this purpose.

Finally, explainability and auditability need to be baked in throughout the design process rather than being bolted on after the fact, particularly when regulators ask why a particular lending decision was made or compliance officers need to understand how a model’s behaviour has changed over time. The architecture should provide clear answers through comprehensive logging, versioning and monitoring capabilities built into the foundation of the AI platform.

The Architecture Advantage

Institutions pulling ahead aren’t those with the most advanced algorithms or biggest data teams, but those running AI reliably in production, learning from real-world performance, and scaling what works to turn innovation into advantage.

Cloud-native architecture is key for financial institutions as its features align with AI at scale. Elastic scalability handles variable workloads, while microservices enable modular systems that evolve with AI, thereby supporting experimentation without compromising stability

Emerging architectural patterns include infrastructure for Edge LLM

distributed learning models, which support AI data sovereignty by keeping data local, on devices or within regional boundaries. This ensures compliance with privacy regulations and reduces infrastructure costs by minimising centralised processing and cloud usage, improving AI efficiency, lowering latency and reducing expenses.

The regulatory environment reinforces this trend. As AI governance, risk management and resilience expectations evolve, banks with modern architecture will find compliance far easier than those retrofitting legacy systems.

The Foundation Question

AI is already delivering measurable value for financial institutions. The difference between why some banks are capturing that value while others are still running pilots, is whether their infrastructure can actually support the AI ambitions they’re funding.

Waiting to invest in proper architecture until AI proves itself creates a paradox, given that without the right foundation, AI can’t prove itself in any meaningful way. The models might work in controlled environments, but production is where revenue gets generated and competitive advantage gets built. Banks that postpone architecture decisions will likely learn the hard way that “later” means losing ground to competitors already scaling what works.

Dean Clark, CTO, GFT

John O’Brien, CEO, NashTech

Bridging the AI gap in banking: From pilot projects to proven performance

Across the ﬁnancial sector, the race to deploy generative AI assistants is gathering pace. From tools that help employees surface information faster to systems supporting customer service teams, banks are investing heavily in proofs of concept. Yet despite strong early results, many are ﬁnding the leap from pilot to production harder than expected.

Recent research from UK Finance shows that more than 70 per cent of financial institutions’ AI initiatives remain in pilot or limited-scale use. While banks clearly see the potential of AI to boost productivity and decision-making, safe and scalable implementation demands a different mindset, one that balances innovation with compliance, governance and data integrity.

Why scaling remains difficult

The challenge is not a lack of ambition. Most banks recognise that AI can transform operations and improve customer experience. The friction arises when early experiments meet real-world complexity, including legacy systems, fragmented data and strict regulatory oversight.

An AI pilot often performs impressively in isolation, but connecting it to live data or existing infrastructure introduces risk. Concerns around data privacy, model transparency and auditability slow momentum. The result is progress that feels incremental rather than transformational.

As I often tell clients, the success of AI is rarely about the algorithm, it’s about the ecosystem around it. Without strong data foundations, clear governance and the right human oversight, even the smartest models will struggle to deliver sustainable value.

A glimpse of what’s possible

Elsewhere, there are promising examples of what happens when AI is embedded effectively. VietinBank, one of Vietnam’s

largest and most prestigious banks, partnered with NashTech to build a secure internal AI assistant using the company’s BonBon AI platform on Microsoft Azure.

Trained on more than 2,000 internal documents, regulations and policies, the assistant, known as Genie, now handles hundreds of thousands of staff queries each month. It has reduced Q&A times by up to 95 per cent, freeing employees to focus on higher-value work and earning VietinBank national recognition for digital innovation.

While the project was led in Asia, the same blueprint of deep integration with core systems, clear governance and a people-first design mirrors exactly what UK banks must now master to move from experimentation to enterprise scale.

Building safe, scalable AI

For most retail banks, three priorities will determine whether AI moves from pilot to performance:

1.Integration over isolation. Design pilots that connect seamlessly with existing systems. Success depends on interoperability, not novelty.

2.Governance as an enabler. Compliance and auditability should be built into every stage of AI development, not added as barriers afterwards.

3.Human-centric design. AI assistants deliver greatest value when they empower employees and customers, not when they attempt to replace them.

Another common mistake is treating AI initiatives as one-off experiments rather than continuous programmes. Banks that establish centralised AI governance frameworks linking data quality, ethics and security will be best positioned to accelerate adoption safely.

The next phase of digital transformation

UK banks have long led the world in fintech and open banking. Applying that same disciplined innovation to AI can unlock a new wave of efficiency and service quality. Institutions that combine technical rigour with a focus on trust and transparency will move fastest.

AI assistants will not transform banking overnight, but they are already redefining

how information flows through an organisation. When implemented responsibly, they cut through inefficiency, support better decisions and give employees more time for customers.

The next era of digital transformation will not be judged by how many pilots are launched, but by how effectively they deliver measurable, compliant and lasting value.

About the Author

John O’Brien is CEO of NashTech, a UKheadquartered global technology consultancy specialising in digital transformation and software engineering. The company partners with leading ﬁnancial institutions to deliver secure, scalable AI and automation solutions that drive measurable business value.

How women can drive the future of finance

Radhika Kapur, VP EMEA Partnerships & Technology, Confluent

Let’s be honest: being a woman in the worlds of finance and technology can still feel like walking into a room where everyone else received a briefing you somehow missed. You sit down, look around, and suddenly realise — it’s just you. Again.

I’ve lost count of the number of women, from junior analysts to senior leaders, who’ve told me some version of the same story. And it’s not because there’s a lack of talent. Far from it. The pipeline is full of brilliant, capable women who could be driving some of the biggest

innovations in data, trading, risk, and financial infrastructure.

So why aren’t more of us there? Research continues to show that structural barriers persist. A recent report from the trade union Eurocadres has found that only 19.5% of administration and finance management roles are held by women — a clear reminder of how deep the leadership gap still runs in a sector that shapes major business decisions, even in countries with strong equality policies on paper.

These gaps reflect systems that still need work. And until they close, we need something powerful: self-championing. The decision to speak for yourself and take ownership of your path even when the environment isn’t immediately built for you is an incredibly powerful one.

Here’s what I’ve learned through my own experience and stories of women around me:

1. Your career doesn’t have to be conventional to be impressive

Some of the most successful women I’ve met didn’t start where they ended up.

One professional I met began in chemistry, later discovered a passion for data science, and ultimately moved into software development. Another started in international marketing before transitioning into analytics and partnership roles.

If you’ve paused and restarted, that isn’t a weakness. That’s experience and courage.

In fields like finance and tech, where change is constant and adaptability is gold, a non-linear background is often a secret advantage. You can offer transferrable skills, and distinct perspectives based on your experience, that nobody else can.

2. Build a support squad

Here’s a crucial lesson: being independent doesn’t mean you have to sacrifice the support of others.

I like to think of it as your “personal board of directors” — a small group of people who each play a different role in your growth.

Its members can take all forms. There’s the sponsor who brings your name into rooms you’re not in; the mentor who tells you honestly when you’re underselling yourself; the peer who shares

opportunities and celebrates every win with genuine enthusiasm.

And here’s the magic: you should be that person for others, too. The more we lift one another, the faster we all rise. Women supporting women is one of the strongest ways to grow together.

3. Soft skills make a difference

In complex industries like finance where metrics, method and precision are so highly prized, it’s important not to forget that the people behind them are just as important. The soft skills that motivate, heal, and empower others can be critical to any meaningful success.

I once worked with a woman who was told her feedback style was “too robotic” for leadership. She’d made the assumption that being authoritative and stern was what her workplace needed from her — but in bringing more of her natural, warm self to her workplace, she was able to affect change much more powerfully than before.

The women who truly stand out, especially in finance, are the ones who can translate complexity into something human, and then lead with empathy.

4. Ignore imposter syndrome!

I’ve met women managing billion-dollar portfolios who still say, “I’m not sure I’m qualified for that.” That certainty can keep you waiting a very, very long time for the ‘right opportunity’ to come around.

At the same time, I’ve watched others apply for opportunities they were only partly ready for, get them, and excel. Immersing yourself in a challenge that you have to apply yourself to succeed in isn’t only the best way to learn. It’s exciting!

If you find yourself hesitating because you think you need more experience or more proof, take a moment and ask: would

someone else already see me as capable of this?

If the answer is yes — take a step forward. Confidence grows through action.

5. Your visibility creates room for others

Representation really does matter. When women see other women in finance and tech, it quietly shifts what they believe is possible.

In fact, a recent study published by the Institute of Electrical and Electronics Engineers (IEEE) found that women exposed to female role models were 2.6 times more likely to stay in engineering careers. When you can see someone who looks like you doing the job, it’s easier to imagine yourself there too.

So, when you put yourself forward — for a project, a promotion, or a new opportunity — you’re not just backing yourself. You’re helping someone else see that they belong here as well. That’s how change starts to grow.

The bigger shift

Women remain underrepresented in finance, tech, and leadership. That’s still the reality. But conversations about fairness and empowerment grow every year, and so does our willingness to back ourselves and each other.

Progress comes from owning our journeys, speaking up for our growth, and helping create workplaces where more women can advance. Becoming your own career champion means recognising your capability and stepping into spaces you’ve earned.

When we do that while supporting the women around us, the impact multiplies. A conscious choice to champion ourselves and others is the route to the recognition that we deserve.

Sprawling cloud cost is the risk that accountants are overlooking

Paul McAdam, Director, Source Code Control

Cloud adoption has accelerated across almost every industry, boosting productivity and scaling innovation. Yet, while the benefits are clear, a growing financial risk has been developing in the background - uncontrolled cloud costs.

- Paul McAdam

In many organisations, cloud consumption has outpaced governance. Services are spun up in minutes and deployed without oversight, workloads are moved, and data requirements expand continuously. Often, there is little visibility into who is responsible for what, how efficiently those resources are being deployed and whether older versions are still running unnecessarily.

Add in a lack of understanding about the multitude of billing models that cloud providers use – from pay-as-you-go to data egress fees. The result is a term known as “cloud sprawl”; a tangle of unmonitored, duplicated and outdated resources that steadily inflate bills.

For many organisations, this isn’t a minor inefficiency; it’s a substantial and growing financial exposure. Cloud spend is now one of the largest unconstrained costs and it can directly erode margins and destabilise budgets if not managed effectively. Yet, surprisingly, few accountants

are actively assessing it as part of their risk reviews.

The case for FinOps

The emerging discipline of FinOps – short for Financial Operations – provides a structured approach to managing cloud expenditure. It combines financial accountability, operational awareness, and technical optimisation to ensure that cloud investments deliver value. FinOps encourages collaboration between finance, IT and business units to align cloud consumption with strategic and budgetary goals.

For accountants, partnering with FinOps specialists represents a significant opportunity. Just as they already manage cost controls for payroll, procurement and capital expenditure, they can bring the same financial rigour to cloud operations.

Incorporating FinOps into governance frameworks, accountants can help businesses to identify inefficiencies, as regular

monitoring can uncover over-provisioned resources, redundant services and outdated services that are still incurring costs.

They can also optimise spend, with analysis by Source Code Control showing that an average of 23% of cloud expenditure can be reduced through better management and optimisation, delivering immediate cost savings.

Additionally, accountants can improve financial forecasting. The variable bills common in cloud costs are difficult to predict, but FinOps practices introduce data-driven forecasting which helps to stabilise budgets.

Enhancing compliance is a further benefit, as many regulations now require transparency in IT and data management costs, and applying financial governance to cloud use supports audit readiness and regulatory adherence.

By engaging early and embedding financial oversight into the cloud lifecycle,

accountants can transform a potential cost risk into a managed investment.

Cloud cost as an unconstrained risk

To treat cloud cost as a financial liability, organisations must first achieve visibility. This means creating an accurate inventory of cloud assets and understanding how each service contributes to business value. Once visibility is achieved, the next step is to implement controls – setting budgets, tracking usage against those limits, and enforcing policies for resourcing.

In the world of FinOps this is known as “showback” or “chargeback”.

Automation also has a critical role to play. Cloud management tools can automatically scale cloud services down during periods of low demand, decommission idle instances and flag unusual spending patterns. These are the kinds of proactive services that accountants should be advocating for as part of their oversight responsibilities. But be careful, as some of the tools are expensive and over-featured,

resulting in what customers perceive as a “tax” on their cloud costs.

An emerging opportunity for accountants

For accountants, cloud expenditure represents both an opportunity and a challenge. Effective management of cloud costs can drive real financial outcomes; therefore accountants can offer greater strategic value by integrating them into audits, risk assessments and financial planning. It presents a new area of financial stewardship that reduces business risk and strengthens their own relevance and profile in digital finance. And it's just as applicable to their SMB, education and public sector customers, as to large enterprises.

Those accountants who build expertise in FinOps and cloud accountability won’t just contain costs and manage risks; they’ll shape strategy, drive efficiency, and prove their value as essential partners, bringing financial governance into fast-moving cloud-based operations.

Why ﬁnancial inclusion is only half the job

- Jonty Rawlins, Director of Sustainability at Platcorp

In financial services, inclusion is often measured through access milestones: the number of accounts opened, loans disbursed, or branches expanded. Yet such metrics only tell part of the story. Access statistics don’t show whether people’s lives are actually improving or whether these new services have a tangible impact on those they’re meant to help. Ignoring impact while celebrating ‘inclusion’ is at best a job half-done at worst an arguably fruitless exercise.

Access versus impact

There’s an important distinction between access and impact but too often the two are blurred. Access, such as increasing the number of bank accounts opened, is an input, but impact (for instance, the number of people living below the poverty line) is an outcome. However, many in financial services are treating these inputs as the end goal, measuring success by the services they set up and mislabelling inputs as outcomes. This muddying of terms

means the effects of inclusion efforts aren’t properly examined; a dangerous and wasteful result that divorces action from responsibility.

If we want to learn how inclusion efforts are shaping people’s lives, we need to zero in on impact. This means examining who is benefitting from services and how outcomes are distributed among different demographics: for example, among women, youth, or those in rural areas. It means shifting the focus from reporting

what was done, to demonstrating what changed; measuring success by the outcome and not by the input.

Data is the next frontier in financial inclusion

Central to this shift is exploring new ways to assess impact. One such way is by using the Poverty Probability Index (PPI) as a metric: a validated, 10-question, country-specific poverty measurement tool, maintained by Innovations for Poverty Action, that estimates the likelihood a household lives below a chosen poverty line.

The PPI is built from national household survey data and is used by hundreds of organisations globally, including ourselves, to allocate resources and track progress. When combined with behavioural and portfolio signals, it enables organisations to refine pricing, tenors and service models for different client segments, as well as signalling when to layer in non-financial services (such as around financial literacy or agri-extension) for the clients who are

most at risk of falling behind. It’s not about labelling people, it’s about making sure products and capital are reaching those who need it most.

Measurements like PPI are critical in helping organisations monitor whether financial products are affordable, sustainable, and truly effective for low-income customers. Additionally, they provide a framework for greater governance and accountability, helping microfinance providers meet the industry's Universal Standards for Social and Environmental Performance Management, which calls for client-centred strategies, monitored outcomes, and responsible growth. A recognised poverty measure, like PPI, helps organisations fulfil these requirements while aligning them with investor expectations on consistent, evidence-based impact.

The business case for measuring poverty

Focusing on impact over access is crucial in meeting ethical standards and building investor confidence; ensuring cost of

funds is aligned with the value created, but these aren’t the only arguments in the business case for measuring poverty. PPI allows organisations to gain segment-specific insights that help them fine-tune product features, reduce drop-outs and improve client retention, especially among first-time borrowers.

In-depth understanding of client vulnerability is also essential in building effective early-warning systems and hardship protocols to better serve clients when shocks hit. These features both strengthen portfolio quality and allow an organisation to offer a higher level of client protection. All these benefits are yet another demonstration that ethics and smart business strategy don’t have to be opposed.

Switching the focus from access to impact is a small but incredibly powerful move. It means attention is clearly trained on clients’ real needs, not what sounds good in a press release. Comparable, verifiable outcome data is a crucial tool in breaking poverty cycles and ensuring services are as effective as they can be.

Written By Matt Kent

Lowering business costs by bridging the data divide

For a business to operate successfully, every team needs to perform its vital function. For many organisations, that includes managing buildings and assets through third parties. With Business Rates set to increase for all premises with a rateable value over £500,000 in 2026, supply chain collaboration is critical for identifying cost reduction opportunities and maximising efﬁciency.

But that is easier said than done if building data is siloed into separate operations. The industry standard for building maintenance, SFG20, recently found that only one in 10 professionals in the field have accurate asset registers, and that onethird keep their registers in spreadsheets that don’t feed into wider operations. This disconnect between data collection and

usage means that businesses are missing opportunities to use data for effective decision-making.

To bridge that gap, organisations need to review their asset strategy and work with specialists to implement accessible systems that connect operational data with boardroom decisions.

- Matt Kent, Director Of Engineering, EMCOR UK

A structured approach

Developing a strategic plan requires a framework that identifies the intersection between possible performance improvements, cost implications, and risk factors. Think of it as a Venn diagram approach that reveals trade-offs and opportunities.

This framework shows where enhanced performance may justify increased investment or highlight where cost reduction won't compromise operational integrity. The key is aligning asset management decisions with broader business objectives, whether that's meeting sustainability targets, improving operational resilience, or reducing total cost of ownership.

Should an ageing but critical asset be maintained more frequently, upgraded with monitoring technology, or replaced entirely? The answer depends on understanding both the asset's condition and its operational importance to business continuity.

For example, as the UK Government aims to reach net zero by 2050, businesses face

increasing pressure to demonstrate progress on decarbonisation commitments. By considering age, criticality, maintenance history, and breakdown patterns of an asset, engineering teams can identify inefficient systems and prioritise investing in sustainable infrastructure.

Developing a collaborative approach

Once that framework is in place and asset data is being collected, an insight platform can break down data silos. A digital hub can collate information, clean it, create a single repository, and report asset performance data across facilities. These insights can then be fed into wider leadership planning meetings and aligned with organisational goals.

Modern insight platforms might rely on a range of technologies, including interactive virtual modelling, diagnostics tools, geolocation, Internet of Things, sensors, and intelligent analytics. Each

supports proactive rather than reactive decision-making.

Once systems are configured to monitor asset conditions, report compliance metrics, and track uptime statistics, they feed directly into lifecycle decisions. For example, our team recently conducted targeted survey inspections for 28,500 assets, generating over 280,000 data points for a customer across multiple sites, reviewing criticality, condition and lifecycle information.

The data helped address inefficiencies and allowed us to develop an alternative maintenance approach, based on business-focused maintenance principles, that maximised asset lifespan. It also reduced the time allocated to planned maintenance by 35 per cent, which freed up technician capacity to focus on immediate needs and continuous improvement initiatives.

Mind the gap

Business investment in integrated data systems may increase upfront costs, but the return on investment becomes clear when operational decisions are informed by accurate, accessible information.

Shifting from schedule-based maintenance, where assets are serviced at fixed intervals regardless of need, to condition-based and predictive maintenance that responds to each asset’s actual health can extend lifespans and reduce time and money spent on unnecessary interventions. That leads to better budget predictability, reduced unplanned downtime costs, and improved planning for asset replacement.

When asset information flows seamlessly from the building floor to the boardroom, engineering, facilities, and leadership teams can work together to balance cost needs, performance requirements, and risk management, driving long-term business objectives.

Financial inclusion begins with infrastructure, not access

Financial inclusion is one of the industry’s most repeated promises. It appears in strategy decks, on global policy stages, and in in-depth ESG reports. The intention is sincere, but the understanding and execution remain uneven. And too often, the discussion defaults to a simplified storyline about banking the unbanked, as if access to an account, by itself, unlocks full economic participation.

Spend any time with institutions operating across growth markets and a different truth emerges. Financial inclusion is not a retail challenge, so account creation is not the sole solution. It is an infrastructure challenge, and the real barrier is connectivity.

When access outpaces capability

A microbusiness in Nairobi can open a bank account in minutes, yet paying a supplier in São Paulo can still take days. A family in Manila can receive digital wallet transfers instantly, yet those funds can take far longer to clear through the banking system. And a family in Lagos may still face a maze of intermediaries

and unclear fees when trying to pay their daughter’s tuition in Warsaw.

These are systemic consequences of how institutions verify, connect, manage risk, and settle value. You can give a family a bank account, but if the institution behind that account cannot transact with the rest of the world efficiently, the inclusion is superficial. The door opens, but the hallway leads nowhere.

The illusion of progress

It’s typical for the industry to focus on key metrics around account growth, volume of digital IDs, or more activated wallets, however while these gain matter, they do not tell the full story. Despite billions gaining formal access in the past decade, global remittance costs remain stubbornly high, especially in regions like Sub-Saharan Africa where families still pay a premium just to move their own money.

The underlying issue is interoperability. Access without the ability to meaningfully participate in global flows does not result in true transformation. The decline in correspondent banking relationships

which is down significantly over the past decade further exacerbates this problem. Many institutions in emerging markets have lost direct global links, relying instead on elongated chains of intermediaries that add cost, delay, and risk.

When the rails weaken, inclusion fractures.

The digital asset distraction

Layered onto this is the industry’s fascination with stablecoins and tokenised assets. These technologies offer genuine potential in the form of programmable payments, transparent settlement, improved liquidity, but they do not solve the financial inclusion challenge on their own.

They only matter if regulated financial institutions can adopt them at scale, however many can’t. Not because they lack curiosity, but because their risk frameworks, regulatory environments, and operational models are not yet ready. Elegant technology is irrelevant if institutions cannot confidently step onto the bridge it promises to build.

Fragmented rails across regions

Across markets, the pattern repeats:

In Latin America, consumer adoption is fast, and fintech innovation is culturally embedded. Yet cross-border payments remain pricey because institutional links lag far behind the customer enthusiasm powering the region’s fintech boom.

In Africa mobile money reshaped domestic commerce, proving what leapfrogging can achieve, but cross-border settlement remains slow and costly because many receiving institutions lack efficient links into global networks.

Asia is often treated as a single market, but in reality defined by vast regulatory, cultural, and legal diversity. Successful institutions thrive because they adapt to complexity rather than trying to erase it.

In Europe mature infrastructure and instant domestic schemes coexist with uneven cross-border operability, showing that even advanced markets reveal that institutional connectivity still has blind spots.

The constant lesson across the globe is that if institutions are empowered, people are too.

Is AI a help or hinderance?

Artificial intelligence is accelerating a divide between institutions that can operate in real time and those that cannot. Banks are adopting AI for risk scoring, anomaly detection, and dynamic compliance through tools that can transform compliance from a chokepoint to a competitive advantage.

But AI is yet to provide a true and proper fix for weak infrastructure, it simply spotlights it. Institutions with modern connectivity will use AI to compress settlement times and strengthen trust. Institutions without it will simply automate delay.

What meaningful inclusion actually requires

True inclusion is functional, not cosmetic. It requires institutions that can:

• Verify counterparties instantly

• Assess and price risk in real time

• Settle payments without long chains of intermediaries

• Comply across borders without throttling throughput

• Operate at the speed global commerce expects

When institutions can do these things, the benefits flow quickly and we will start to see cheaper remittances, more predictable trade, stronger household resilience, and broader economic participation.

A more grounded definition

Financial inclusion is the structural ability for money to move where it needs to go without friction. It is the network effect created when institutions can trust each other, transact directly, and settle value efficiently.

We will not achieve meaningful inclusion by opening more accounts or launching more apps. We will achieve it by building the infrastructure that lets those accounts, and the people behind them, participate fully.

Financial inclusion is infrastructure. Everything else is decoration.

Fintech vs old tech: The UK must create a new vision

By Gianluca Berghella, CEO of Armundia Group

For decades the UK has held its position as one of the world’s most important financial centres, occupying a prominent place in the global financial imagination.

Even despite the macro instability dominating investors’ outlook, in 2025 the number of financial services companies intending to increase their UK investments has nearly doubled — a 90% increase — according to EY’s UK Attractiveness Survey. And in 2023-4, the UK attracted £4.1bn in fintech investment, becoming the world’s second-largest fintech ecosystem after the US. What is behind the UK’s enduring appeal, and why is it increasingly at risk

The UK’s fintech strengths

The UK has one of the world’s most dynamic and advanced financial ecosystems, supported by a deep talent pool, progressive regulation, government-backed fintech initiatives and robust digital infrastructure. Furthermore, the UK is not just

one market; it’s a gateway to the world. For European banks and insurers looking to expand globally, for example to the Middle East, often the best route isn’t the most direct, but via the launchpad of the UK.

Another distinct feature of the UK market is its structural diversity. While insurance and banking are tightly integrated across most of Europe, in the UK they operate as distinct sectors. This differentiation can be a barrier to entry — but also a catalyst for strategic partnerships and M&A. The UK isn’t only a market to serve, but an ecosystem to collaborate with.

All these elements make the UK an ideal environment for European institutions — private banks, asset and wealth managers, insurers — seeking both scale and innovation.

However, the UK’s future position as a global finance hub isn’t guaranteed. With a tide of disruptive technologies already reshaping the fintech sector, the UK’s

traditional approaches to banking may put it under threat. Its financial infrastructure, though strong, faces the same modernisation challenges as experienced across Europe.

These challenges and opportunities are precisely why Armundia Group has expanded into the UK within the past year. We are committed to bringing our vision and innovative technological model to the market. Our goal is to overcome the limitations of traditional approaches and legacy infrastructures, by offering modular, scalable solutions that help financial institutions reimagine their services.

So, in a disruptive digital-first environment, how can the UK retain its prominence? A renewed digital vision is required.

Old infrastructure demands a new vision

To maintain its position as an international finance hub, the UK must build a

more modernised tech foundation; this is what the market increasingly demands. Failing to do this — continuing with traditional business models using legacy software — will only limit the UK’s future competitiveness.

While the UK’s financial outlook is undeniably modern and forward-looking, its underlying systems and banking infrastructure remain deeply traditional. In investment management especially, many institutions still rely on legacy operating models rather than embracing full digitalisation. Bridging this gap between tradition and digital transformation is the real challenge ahead.

Answering the challenge doesn’t necessarily require institutions to embark upon major multi-year digital transformation programmes, nor hire a Chief AI Officer or build a bespoke tool in-house. In fact, pursuing large-scale transformation is often counter-productive.

Instead, the next logical step for UK institutions seeking to remain competitive is to leverage ad-hoc technological solutions. The focus should be on gradually integrating these digital processes in a targeted and effective way. There are many success stories from digitalisation activities in Italy, where systems have been reimagined across financial services and public administration.

The UK stands at a fascinating crossroads. It has an opportunity for a refreshed vision based on best practice and sustainable innovation. What’s required is an open-minded, disruptive approach, and for that vision to be enabled by reliable technology. What will these transformed services look like? That’s for financial institutions to discover in collaboration with their technology partners.

The global finance sector is ready to reimagine services and reshape the way societies engage with money—will the UK be the hub for this too?

Sources:

� EY UK Attractiveness Survey, June 2025

� TheCityUK, January 2025

� Techfundingnews, March 2025

� Retail Banking International, June 2025

- Gianluca Berghella

THE BUDGET: Tech Sector Needs Clarity and Reform on R&D

Complexity That Stifles Innovation

Jake Rickhuss, MD Commercial and Co-Founder of Tech Consultancy, Journi

As the government prepares its autumn budget, there is arguably more concern and speculation about what will be in it than ever before. The UK’s tech sector remains one of our most dynamic engines of growth, contributing billions of pounds to the economy and employing millions across startups, scale-ups, and global enterprises. Many in tech are looking for grand gestures to support the sector. I ask for just one, clarity in research and development (R&D) tax relief. Simplifying and stabilising the system could be one of the most effective ways to unlock innovation and investment and turbo-charge productivity growth in the UK tech sector.

At present, the UK’s R&D tax relief framework is ambitious but ambiguous. The system is designed to incentivise innovation, yet its complexity often discourages it.

The current system leaves businesses questioning what is and isn’t applicable and ultimately what actually qualifies as an R&D activity. This naturally has a knock-on effect, which in turn delays investments and projects. Providing clarity and a simpler system would give companies the confidence to innovate, scale new digital products and grow without having to second-guess the process.

When companies can’t plan confidently around R&D incentives, they hesitate to commit resources, delay launches, and sometimes even shift their innovation activities abroad to jurisdictions with clearer frameworks.

Why the Budget Matters

This autumn’s budget provides a critical opportunity for the government to reaffirm its commitment to making the UK a global leader in science, technology, and innovation. The Treasury has already recognised the need to consolidate and modernise the R&D tax relief system. Yet, implementation has been patchy, and many of the small and medium-sized enterprises (SMEs) that we work with, the lifeblood of the UK tech ecosystem, remain uncertain about how the merged schemes will work in practice.

A budget that prioritises simplification, transparency, and accessibility could have a transformative effect. It would stimulate both private-sector innovation and the broader supply chain, from software and AI development to green technologies and advanced manufacturing.

Building a Framework for Growth

Policymakers should also consider how the structure of relief supports different stages of business growth. Startups, for instance, may benefit more from upfront credits or grants to support early-stage development, while larger firms might prioritise incentives that reward scaling and export activity.

There is also scope for better alignment between R&D tax relief and other government initiatives, such as the new UK Advanced Research and Invention Agency (ARIA) and regional investment zones. A coherent strategy connecting these programmes could create an innovation pipeline that moves seamlessly from concept to commercialisation, driving sustainable growth and regional economic balance.

Confidence Through Consistency

Aclear, stable, and well-communicated system would demonstrate that the UK is serious about supporting innovators, that it’s not just the same old rhetoric we get from every government.

The UK has the talent, infrastructure, and entrepreneurial spirit to lead the next wave of global tech innovation. What’s needed now is a tax and policy environment that matches that ambition, one where clarity replaces confusion, and complexity gives way to confidence. This budget offers a crucial opportunity to make that shift and empower UK technology to lead the world.

Trading on borrowed time: How AI can rescue ageing platforms

Alexander Goncharuk, Managing Director, UK, and Global Head of FSI at Intellias

Many financial institutions still depend on trading platforms built decades ago — ones that were never designed for today’s data intensity, regulatory pressures, or the expectations of algorithmic trading.

And although they’ve served the industry well, they now constrain performance, complicate integration, and increase compliance and operational risk.

But when critical workloads live inside long-entrenched architectures, the road to innovations can feel daunting. The good news is that firms no longer need to choose between risky ‘big bang’ replacements and indefinite patchwork upgrades.

Modernisation approaches and the growing role of AI

Finding the right modernisation strategy for your business should depend on a clear understanding of your data architecture, business priorities and appetite for disruption. Only when you’ve defined this, will you know which approach is right for you.

Most organisations pursue one (or more) of these four paths:

1. Incremental modernisation - controlled progress and early wins

Incremental modernisation breaks the journey into manageable phases, allowing institutions to upgrade high-value components first, without compromising day-to-day operations.

A typical sequence might begin with order management and execution engines, before moving to reporting, analytics, or external application programme interface (APIs) — and AI is becoming increasingly central to this approach.

AI dramatically reduces the time these approaches require, which frees developers to focus on redesigning high-value components rather than deciphering decades-old codebases.

3. Mainframe migration - escaping structural limitations

Many trading platforms still operate on mainframes built for throughput, not flexibility which modern markets require. Migrating these workloads to cloud-native environments enables firms to scale elastically, reduce hardware costs, and introduce modern analytics and low-latency data pipelines.

Mainframe migration no longer mandates rewriting everything from scratch. AI-powered tools can translate legacy languages into modern equivalents, analyse code for hidden dependencies, and optimise performance during and after the move, which significantly reduces the traditional risks associated with migration.

4. Hybrid modernisation - evolving without disruption

Automated dependency analysis can map the relationship between services and data flows, helping teams to identify the safest and most beneficial areas to modernise first. AI-based backlog generation and prioritisation can then accelerate planning while reducing the likelihood of overlooking hidden risks.

2. Replatforming or refactoring - modern capabilities without full replacement

Replatforming moves applications and data from legacy environments to modern cloud-native infrastructure, while keeping business logic intact. But refactoring goes further by reengineering the code, decomposing monoliths into microservices, updating APIs, and enhancing data pipelines.

Hybrid strategies help institutions combine legacy systems with modern, modular services. APIs and integration layers allow both environments to operate in parallel, enabling organisations to phase out outdated components over time.

AI strengthens this model by orchestrating workloads, mapping integration layers, synchronising data across environments, and monitoring the whole system for performance anomalies — striking the ideal balance between stability and long-term transformation.

Modernisation delivers measurable value

Modernisation shouldn’t just be a response to ageing systems. When executed well, it’s a catalyst for competitive advantage:

- Operational efficiency rises: Modern architectures, supported by AI automation and an API-first model, streamline processes across the trade lifecycle.

- Deployment cycles accelerate: Cloud-native environments and continuous delivery pipelines make it possible to release new features quickly. Plus, AI-driven refactoring and automated testing compress delivery timelines even further.

- Traders get a better experience: With low-latency execution engines, real-time data processing, and intelligent insights, modern platforms strengthen both user experience and trading outcomes.

- Technical debt declines: Replacing fragile workarounds with modular, scalable components reduces maintenance overheads and improves long-term cost efficiency.

- Resilience and compliance improve: Modern systems embed traceability, data lineage, and audit-ready reporting from day one, which reduces the burden of regulatory change management.

1. Begin with a rigorous risk and value assessment: A thorough evaluation of ROI, interdependencies, and operational impact, helps teams prioritise initiatives that deliver the highest value with the least disruption.

2. Use automated testing from day one: AI-enabled testing increases coverage across APIs, data pipelines, and interfaces, reducing the chance of costly errors reaching production.

- The foundation is laid for future innovation: With AI-enabled infrastructure, microservices, and cloud integration in place, firms gain the flexibility to adopt new models, expand into emerging markets, and respond swiftly to technological change.

Practical steps to mitigate risk

Given the major technological and operational challenges involved, there’s no escaping that modernisation will come with a dose of risk. But when approached systematically, most of that risk is predictable, and manageable.

3. Automate data-integrity checks: Automated validation ensures order histories, trade records, and reference data migrate accurately, which is critical for compliance and audit readiness.

4. Monitor continuously: AI-driven monitoring tools detect anomalies in latency, throughput, and trading activity in real time, enabling proactive intervention before issues escalate.

Ageing trading systems may still function today, but they are fast becoming liabilities. With AI-enabled tools and a clear, strategic approach, financial institutions can modernise with confidence, unlocking the speed, resilience, and intelligence needed for the next era of capital markets.

For firms, modernisation risk can be managed with the following best practices:

Alexander Goncharuk, Managing Director, UK, and Global Head of FSI at Intellias

Alexander Goncharuk is a financial services technology leader and Managing Director, UK, and Global Head of FSI at Intellias, where he leads large-scale implementation, integration, and transformation programmes for banks, insurers, and fintechs. Previously, he held Front Office and Market Risk Technology roles at JPMorgan Chase, Goldman Sachs, Standard Bank, and Trafigura in the UK and US, specialising in regulatory frameworks. Known for using technology to solve business problems, he partners closely with executives and engineering teams to deliver measurable results.

Toward a Sustainable Plastics Economy: Technological Innovations Reshaping Global Business

The global plastics industry is one of the largest industrial systems in the world, touching nearly every sector of the economy. Estimates commonly place its value in the trillions of dollars when accounting for production, logistics, consumer goods, packaging, waste management, and recycling infrastructures.

However, this vast scale comes together with a profound environmental footprint. As we confront growing concerns about climate change, biodiversity loss, and resource depletion, it has become increasingly clear that the traditional model of plastics—heavily dependent on fossil fuels—can no longer serve as the foundation of a sustainable future.

In recent years, scientific and technological innovations have offered a compelling path forward: a shift from fossil-based plastics and first-generation biofuels toward advanced bio-based plastics derived from renewable biological sources.

These new materials, produced from feedstocks such as agricultural residues, algae, or even captured carbon, promise significantly lower greenhouse-gas emissions and reduced ecological harm.

Importantly, the technical capabilities to scale these alternatives now exist. Process innovations in fermentation, polymer chemistry, and biorefining are enabling performance characteristics that increasingly match or surpass traditional plastics.

The problems we observe are not the issues we must solve

My research shows that every transition toward sustainable practices on a global scale is, at its core, a challenge of transforming markets in a sustainable way. As a part of the Dutch Good Growth Fund, my research brings together pioneers, clients and supporting organizations to develop a transition strategy. The research uses analytical models

of the TransMission framework – see TransMission Institute - NewForesightstarting with a root cause analysis why the traditional behavior is so persistent in the plastics industry.

The fossil-based plastics industry is extremely efficient. It benefits from economies of scale, and externalizes many of its harms—such as carbon emissions, waste, and toxic pollution.

This imbalance can be addressed through True Pricing, which makes the real costs of unsustainable practices visible. By quantifying these external impacts, people can make better-informed decisions and reduce the artificial price advantage that fossil-based plastics currently enjoy.

Innovators

face setbacks

However, the transition towards biobased plastics is far from straightforward. Many pioneering companies at

the forefront of bio-based plastics have faced severe financial instability, with some even falling into bankruptcy. These setbacks often stem not from technological failure but from the economic reality of competing against a mature petrochemical industry whose infrastructure has been optimized for decades.

Early innovators typically face high capital expenditures, fluctuating feedstock prices, and uncertain demand. As a result, the path to commercial viability is slippery, even for technically promising solutions.

Despite these obstacles, the momentum toward sustainable plastics is growing. Multinational corporations, pressured by regulatory changes and consumer expectations, are increasingly integrating bio-based materials into their sustainability strategies. This signals a broader shift in business thinking—from treating sustainability as a compliance exercise to viewing it as an engine for innovation, resilience, and long-term value creation.

The role of the financial sector

The financial sector plays an equally critical role. Investors are becoming more sophisticated in assessing environmental, social, and governance (ESG) factors, examining not only risk exposure but also opportunities tied to the circular economy and renewable materials.

Green bonds, impact-investment funds, and blended-finance mechanisms are helping promising technologies move beyond the “valley of death” between pilot and commercial scale.

However, sustainable market transformation of the global plastics industry requires more than capital alone: it demands due diligence, transparency, and a nuanced understanding of how these technologies influence communities, ecosystems, and labor conditions.

The environmental and social impacts of bio-based plastics must therefore remain central to decision-making.

Sustainable biomass sourcing, fair labor practices, land-use considerations, and end-of-life strategies are essential components of a truly responsible plastics transition. A bio-based plastic that contributes to deforestation or undermines food security cannot be considered sustainable, no matter its carbon footprint.

Combining scientific innovation with responsible business practices

Ultimately, the global shift to bio-based plastics represents more than a technological substitution; it is a re-imagining of how we produce, use, and value materials in society.

By combining scientific innovation with responsible business practices, supportive financial frameworks, and a commitment to environmental stewardship, we can transform one of the world’s most entrenched industries into a driver of sustainable progress.

Prof. Dr. André Nijhof, Professor Sustainable Business and Stewardship at Nyenrode

Finance Derivative Magazine Issue 12

FROM THE CEO

Finance Derivative Magazine

What to Expect in This Issue

CONTENTS

CONTENTS

Banking

Technology

Business

Wealth Management

Finance

Winners List

What Investors Should Be Looking Out for in 2026

1. Bet on businesses that know who they are

2. Leadership mindset will make or break growth

3. Watch for clarity, not complexity

4. Relationships will define returns

5. Keep perspective — slow is still progress

The bottom line

Rewiring Global Payments:

Why Infrastructure, Not Innovation, Is Holding Us Back

The Hidden Cost of Fragmentation

Instant Payments, Delayed Operations

The Fix Is Shared Infrastructure, Not More Platforms

Smarter Doesn’t Mean More Complex

Collaboration Is the Bottleneck

Beyond Efficiency: A Global Opportunity

Infrastructure as Competitive Edge

The future of retail payments: What it means for UK businesses

A collaborative approach to execution and delivery

Understanding the Five strategic outcomes for the future of retail payments

Technology as the foundation

Opportunities and challenges

Looking forward and future plans

Agentic AI: A New Era for Insurance Transformation?

The potential of agentic AI

Inevitable challenges

Futureproofing business operations and the workforce

Real-time visibility into risk

The technology advantage

Resilience through better data

Game o� D�ones: The Battle �or the Skies

Responsible AI in Finance: Balancing Innovation with Governance

Risk-modelling, compliance and investment

Challenges

From using finance to creating it

Finance as infrastructure, not integration

A new model for distribution

Why this model succeeds where BaaS faltered

The

AI Is Banking On Data Intelligence

- Roman Stanek, CEO, GoodData

A threefold challenge

From dashboards to decisions

Woman empowerment in ﬁnance

A look at the figures

25% to 50% female representation.

What can women in finance do?

The power of advocacy

AWARDS WINNERS LIST 2025

Winners Titles

Winners

Titles

Winners

Titles

Why Architecture, Not Just Algorithm, Will Define the Future of Financial Services

The production reality gap

Bank-Grade AI actually requires

The Architecture Advantage

The Foundation Question

Bridging the AI gap in banking: From pilot projects to proven performance

Building safe, scalable AI

About the Author

How women can drive the future of finance

The bigger shift

Sprawling cloud cost is the risk that accountants are overlooking

The case for FinOps

An emerging opportunity for accountants

Why ﬁnancial inclusion is only half the job

Access versus impact