AI Playbook for Practitioners in Swiss Banking
THE END OF HYPE,
THE START OF GROWTH
Ethan
B. Schaerer, November 2025
The Executive Focus: 3 Drivers, 3 Risks, 3 Actions
3Drivers:WhyNow
Client expectations have shifted: AI enables proactive insight and personalization at scale, effectively turning every banker intoanaugmentedadvisorwhilekeepingthenecessaryauthenticityofahumanconnection.
Dataisfinallyusable:ModernizedcoresandAPIsfinallymakeusabledatainstantlyaccessible. Regulatoryclarity:FINMAandEUrulesarestable,technology-neutralandactionable.Thisclarityenablesexecution.
3Risks:WhattoWatch
Fragmentedownership:Siloedpilots,nocentralaccountability.
Datawithoutdiscipline:Weaklineageortestingerodestrustandcompliance. Momentumwithoutmeaning:Pilotsthatmovebutdon’tdelivermeasurablevalue.
3Actions:WheretoStart
Establish control and clarity: Build an AI inventory, classify material use cases and align to FINMA (23/1, 18/3, 08/24). Governanceisthecornerstoneofscalability,notabrake.Buildtheoperatingdisciplineonceforglobalrollout.
Deploy for value not curiosity: Prioritize high-impact use cases: i.e., advisory copilots, trading agents, deal sourcing. Use quick-winpilotswithrealP&Ltobuildmomentumandcredibility.
CreateaQRFTeam(Quick Reaction Force): Deploy a small, cross-functional team with deep expertise, emotional intelligence, andauthoritytoact.Theirjob:tomovefast,provevalue,andbuildinternaltrust.Theresultswillspeakforthemselves.
AI is Present Reality, Not Future Potential
FromConcepttoDoing
Over the past year, a flood of white papers has been published to explain artificial intelligence and its potential for financial services.
Most succeed in informing and educating but few translate the concepts into practical applications that executives can act upon. Many decision makers understand that AI is important, yet not all have a clear view of its concrete use cases beyond chatbots or productivitypilots.
TheUrgencyofNow
AI’s inflection point has arrived. Regulation is clear, models are more explainable, and the competitive gap between early adopters and late movers is widening. Therefore, this paper is written from a practitioner’s perspective, a hands-on playbook for high performingteams.Theexamples,usecases,andregulatoryinsightsaredrawnfromreal-worldexperience,includeharddataandare structured to help executives see beyond the hype: how AI can be governed, implemented, and scaled in Wealth-, and Asset Management,InvestmentBankingandFintech.Ihavealsoincludedanexecutiveoverviewwithhardmarketdata,commercialmetrics, and a board-level heat map that illustrate where AI and Multi-Agent Systems are driving measurable results. These insights are designedtogiveleadersaclearviewofwhat’sworking,andwheretherealopportunitieslie.
KeyMessage
Even the most advanced technology delivers no value until it is successfully deployed within the bank’s operational, regulatory and cultural frameworks. AI can be deployed today, under real regulatory and operational constraints, to generate business value that movestheneedle.
Mindset Shift
From Cost Reduction to Growth Engine
The goal is to shift the executive mindset: from viewing AI merely as an efficiency lever or cost-reduction tool, to recognizing it as a strategic engine for competitive differentiation, product innovation, and new revenue models.
The comparison to blockchain is instructive. In its early years, blockchain technology suffered from limited market acceptance and misunderstanding of its practical use cases in finance. Yet today, it is reshaping market infrastructure, from securities issuance and settlement to product design and liquidity access. AI will follow a similar trajectory.
Unlike blockchain, AI is not waiting for market acceptance. The question is no longer “if” AI will scale, but how intelligently each institution will govern and monetize it.
This article cuts through vendor hype and shows where AI delivers measurable ROI in Swiss Banking and a pragmatic roadmap executives can adopt now. AI concepts alone are not enough; success depends on execution within the frameworks defined by FINMA, GDPR, the Swiss FADP, and other supervisory guidelines.
The rules are clear: the challenge is not complexity, it’s doing the homework.
Navigating the AI Risk Jungle: Turning Regulation into a Strategic Advantage
Evolution, not Reinvention
FINMA’s message is clear: the principles remain the same, and their application is now more focused and structured. The guidance does not aim to rewrite the regulatory rules for AI. Instead, it confirms that current technology agnostic approach, governance and risk frameworks still apply. The regulator is neutral regarding technology. It does not dictate which AI models to use or how to code them. It emphasizes that the same principles used for models, data, and outsourcing naturally extend to AI.
From a regulatory standpoint, this is good news. The challenge is not compliance overload but about translation. Banks do not need to create a new framework from scratch. The foundations already exist: model risk management, data governance, operational risk controls, third-party oversight, documentation, and suitability processes. What is needed now is refining what already works. This involves adjusting these frameworks to address AI’s scale, speed, new business opportunities and complexity. In other words, this is an evolution, not a reinvention. The governance wheel does not need to be rebuilt; it just simply needs more clarity. Because clarity builds confidence, and confidence drives speed.
Put simply, the jungle can be navigated by integrating AI into the existing governance architecture. Regulation, if done correctly, is not only a constraint but a strategic enabler, ensuring that AI is implemented safely, transparently, and at scale. Progress in AI is about momentum, not perfection. The winners will be the ones who move with purpose, learn fast, and adapt quickly to add value.
From Practitioner to Practitioner:
I know the phrase ”turning governance into a strategic advantage” has been overused in every board presentation. But in this case, it is true. Once the groundwork has been laid, which allows AI to be deployed across the organization, governance becomes the cornerstone that provides confidence, speed and measurable impact. That’s when governance stops being a cost center and becomes the competitive advantage.
Still not convinced? Look at the firms that are scaling AI responsibly today. They are not moving slower because of governance today; they are moving faster because of it.
Here is my take: clarity drives confidence, confidence drives adoption, and adoption into measurable impact. That’s the real momentum story.
FINMA in Plain Language - What You Need to Do
FINMA's latest guidance 08/2024 is not abstract regulation - it's a practical manual for how to deploy AI responsibly inside a bank.
Here's what this means in plain language:
Governance is non-negotiable: maintain a central AI inventory with clear ownership, oversight, and controls.
Inventory & risk classification: treat every material AI use case as part of a managed portfolio - classify by client impact, risk, and autonomy.
Data quality first: standardize how you collect, clean, and audit training data.
Testing & monitoring: backtest, stress test, and monitor for drift - continuously.
Explainability: if a model affects a client or compliance, you must be able to explain it.
Independent validation: keep development and review functions separate.
Executive Checklist: FINMA-aligned actions next 90 days
Establish or update a central AI inventory and classify applications by materiality.
Assign accountable owners (RACI) for all material systems.
Enforce data quality and provenance standards across internal and vendor sources.
Implement a standard testing playbook: backtest, stress, adversarial.
Put in place continuous performance and drift monitoring.
Document the end-to-end model lifecycle, from purpose to fallback plan.
Commission independent reviews for all material AI systems.
Review third-party contracts for visibility, audit rights, and data usage.
From Practitioner to Practitioner:
“FINMA is clear: technology may evolve, but responsibility does not. Well-governed AI is not only compliant, it's faster to deploy, easier to scale, and infinitely more credible.”
Multi-Agent Systems: Beyond Comprehension, but No Longer Beyond Reach
The emergence of Multi-Agent Systems (MAS) are some of the most talked about developments in Artificial Intelligence, and for good reason. The scale, applicability, and speed of these systems are almost beyond comprehension: intelligent agents that collaborate, reason, and act across data silos, functions, and even organizations. The bottom line is clear: MAS is not just another technological wave, it’s a strategic inflection point.
Here is why you should care:
Banks deploying Multi-Agent Systems report 20-40% faster decision cycles and double digit trading alpha improvement and these results are emerging under real regulatory results.
MAS is fast becoming a commercial lever that will help banks move to the next level of efficiency, agility and revenue growth. They are no longer deployed just to automate workflows, they start building them. This is the jump from copilots to high-value collaborators, a shift that changes everything. It’s worth pausing on that thought.
AI at Work: Real-World Drivers of Growth
Business
Domain
Analyst Research
Productivity
Compliance and Reporting
Mid-, and Back-Office
Workflow Automation
Typical
MAS Capability
Risk Monitoring and Fraud Detection
Agents collaborates on document review, sentiment, ESG extraction and summarization
Agents monitoring regulatory changes, extract and summarize data, automate reporting workflows
Agents orchestrated crossdepartment workflows (i.e., reconciliation, corp. actions)
Agents collaborate: data ingestion, real-time transaction monitoring, anomaly detection, etc.
Commercial KPI
Metric
Analyst output per analyst per month and increased scope
20-60% increase
Business Value for Executives
Manual intervention per 1’000 clients
Cost reduction up to 50%
Lower cost per analyst, redeploy human capital to more value tasks
Compliance becomes a strategic enabler rather than a drag
Manual step reduction, efficiency, FTE cost per transaction
Efficiency gains of 5x10x in some cases
Scalability of operations without proportional FTE increase
False positive reduction, detection speed, real-time, and instant mitigation
Improvement approx.
56% detection accuracy, 41% cost/efficiency
improvement
Better risk control, improved customer experience
The New Revenue Drivers: Multi-Agent Systems
Business
Domain
Investment Banking
(M&A Origination)
Typical
MAS Capability
Trading and Execution
Wealth Management
Agents monitor deal signals, new strategic targets, news, and buyer-seller intent
Commercial KPI
Metric
Improvement
Potential
Lead identification rate, time-to-mandate
+25%-40% increase in qualified leads
Business Value for Executives
Fintech Partnership / Ecosystem
AI trading agents simulate and adapt strategies simultaneously and take advantage of opportunities
Copilots deliver hyper personalized insights, faster and proactive client outreach
Agents coordinate data sharing, automated settlement, API utilization, and client onboarding across partners
Strategy hit rate, and P&L contribution
+10-20% trading Alpha improvement
Faster and more robust pipeline generation and mandate conversion
Direct revenue uplift and reduced slippage
Cross-sell ratio, client engagement, and retention
+15%-25% revenue per client uplift (client segment dependent)
Partner revenue, onboarding conversion
+30% ecosystem monetization increase
More productive relationship coverage, increased stickiness
Strengthened platform economics
From Practitioner to Practitioner:
“Multi-Agent Systems (MAS) is no longer science fiction. They are the next competitive differentiator. The question is not if they will reshape financial services, but who will turn intelligent execution into growth first.”
Staying Relevant
Some clients have put it bluntly: “Meeting a banker can sometimes feel as exciting as going the dentist, unless the conversation is truly worth it.”
The reality highlights a core challenge in Wealth Management: relevance and personalization. Clients don’t just expect competent financial advice. They expect tailored insight, better portfolio performance, proactive thinking, and a sense that their advisor truly understands their situation.
Consider this: a Private Bank can generate thousands of tailored investment insights per client per day but these reports are only valuable if the advisors wish to act upon and when. AI is not a substitute for judgement; it’s a force multiplier.
AI copilots can ensure that client advisors remain unquestionably relevant. They can surface hypertailored client insights and investment ideas at scale, simulate portfolio scenarios and provide actionable portfolio scenarios in real-time to help relationship managers have conversations that matter. The outcome is greater client engagement, deeper trust, and extended coverage without dilution of the human touch. Private Banks using AI advisory copilots have seen a 15%-20% revenue per client increase, and extended coverage ratios without losing the human touch.
Banks and other financial institutions are uniquely positioned to leverage AI, provided deployment respects client privacy, governance rules and risk constraints.
European Regulatory Convergence
This section discusses the adaptation of Swiss financial institutions to evolving regulatory frameworks, emphasizing governance and compliance within the context of the ESMA, BaFin, and EU AI Act.
Across Europe, regulators echo the same logic. Supervisors from BaFin (Germany) to ESMA (EU) and the Bank of England/FCA (UK) have adopted technology-agnostic, principle-based approaches. They all emphasize governance, board accountability, data quality, third-party oversight, and explainability, not the choice of algorithm. The EU AI Act adds a harmonized layer for "high-risk" systems but follows the same principles: classify by risk, document, test, and maintain human oversight. It codifies what good governance already demands.
For Swiss banks operating cross-border, this convergence is good news. It means that branches and subsidiaries can align their AI governance to local expectations without reinventing controls. Existing frameworks-model-risk, data, outsourcing-provide the scaffolding; they need only targeted enhancements for AI: data lineage, explainability metrics, vendor audit rights.
Key Message:
You will find the Board-, and Executive-level regulatory heat map tremendously insightful. It illustrates just how closely global frameworks now align and where they overlap, particularly governance expectations. Put plainly, this is adaptation, not reinvention. Build the operating discipline once, and it covers clear ownership, inventories, testing, monitoring, vendor controls. It meets FINMA's standards at home and equivalent expectations abroad. That alignment is a strategic advantage, not a compliance burden.
Global Perspective: A Common Direction
We find similar patterns across various geographic jurisdictions: the trajectory from Germany's BaFin, through the EU's AI Act to the EBA guidelines, requires traceability, documentation, and human oversight, yet without technology-specific prescription.
The convergence is greater than just within Europe. The United States has seen regulators from the Federal Reserve, OCC, and SEC issue principles that underline model risk management, data integrity, and human oversight-the same pillars echoed in FINMA's approach. The forthcoming U.S. AI Risk Management Framework by NIST and the Treasury's guidance on Responsible AI reinforce that while technology may evolve, governance remains a constant.
Regional financial centers such as Singapore and Hong Kong already have clear statements on AI. The Monetary Authority of Singapore's FEAT Principles stand for fairness, ethics, accountability, and transparency, while the Hong Kong Monetary Authority holds human accountability, explainability, and fairness in its high-level principles on AI. This is the unmistakable direction across all regions: governance first, technology second. For global institutions, that is good news: one governance model, locally adapted, is enough.
One AI Governance Model - Globally Adaptable
Global Convergence - Heat Map
The following heat map provides practical, board-level guidance for global AI implementation. Since most regulators converge on the same principles: accountability, transparency, and explainability, banks can build once and deploy globally, reducing complexity and achieving significant cost savings.
Jurisdiction
Regulatory Stance
Governance Focus
Switzerland (FINMA)
Principle based, technology agnostic Accountability, data integrity, resilience, and documentation
Data and AI Requirements
Alignment with FINMA
Germany (BaFin)
Rules-based, strong on model risk
Model governance, explainability, outsourcing control
Data lineage, suitability, third-party oversight
High alignment
Baseline reference
Strongly aligned
EU AI Act / EBU)
UK (FCA/PRA)
Risk-tiered regulation
Human oversight, data governance
Principle based and proportionality
Explainability, operational resilience
Mandatory for highrisk AI systems
Comparable with FINMA
Strongly aligned
Largely aligned
*Nordics, France, Monaco and The Netherlands: high alignment with FINMA, Benelux: progressive and EU alignment, Austria and Liechtenstein: EU alignment
Visual
Heat Map
Global Convergence - Heat Map
Jurisdiction Regulatory Stance Governance Focus Data and AI
USA (OCC/SEC/FED)
Principle based, technology agnostic
Singapore (MAS)
Hong Kong (HKMA/SFC)
Japan (FSA)
Australia (APRA/ASIC)
UAE (DFSA/CBUAE)
Risk-based, innovation supportive
Principle-based, prudent
Principle based with strong ethical elements
Principle based, innovation supportive
Innovation oriented, proportional governance
Sector driven, fragmented
Fairness, transparency, and explainability
Data governance, model monitoring
Explainability, trust and fairness (use of AI, 2022)
Accountability, explainability and bias prevention
Model risk, independent reviews, bias detection
Guidance based, not prescriptive
Ongoing model performance checks, data quality controls
Partially aligned
Conceptually aligned
Aligned
Human oversight protocols, data ethics standards Aligned
Explainability documentation, model audit, fairness testing Aligned
Explainability, operational resilience
Data residency, cross-border AI system explainability
Partially aligned
From Practitioner to Practitioner:
“Build the operating discipline once, and it covers clear ownership, inventories, testing, monitoring, vendor controls. It meets FINMA's standards at home and equivalent expectations abroad. That alignment is a strategic advantage.”
Clarity is Critical: AI RACI - Deployable Today
At the Executive level, five responsibilities matter most: 1) Define Ai strategy and risk appetite, 2) Assign ownership and accountability, 3) Control data quality, 4) Validate independently, and 5) Monitor continuously.
The matrix includes several critical activities and roles and responsibilities that are often overlooked. However, they are essential for practical implementation, ensuring that governance moves beyond documentation to practical execution. It aligns with FINMA Circular (23/1) Operational Risk, FINMA (18/3) Outsourcing, FINMA Guidance (8/24), EU AI ACT, BankG, FinSa, FinIA, Data protection laws and other.
Activity
Define AI Strategy and Risk Appetite
Description and FINMA Alignment
Primary Roles (A/R)
Supporting Roles (C/I)
Define AI Governance
Framework and Policies
Define AI objectives, ethical principles and acceptable risk thresholds per FINMA’s (23/1) operational risk requirements
A: Executive Management
R: Compliance
Establish policies for model risk, ethics, explainability, data privacy and monitoring (aligned with AI Act)
A: Executive Management
R: Risk and Compliance
C: Risk, Legal, Procurement, IT, Business,
I: BoD, Internal Audit, Procurement/Vendor Mgt, Internal Audit, HR/Training
C: Legal, Data Governance, IT, MRM, Data Science
I: BoD, Internal Audit, HR/Training
Detailed AI RACI Matrix - Deployable Today
Description and FINMA
Identify and Approve
AI Use Cases
Identify AI use cases for business relevance, legal/ regulatory compliance and ethical risks
A: Exec. Mgmt
R: Business, Data Science
Conduct AI Risk Assessment
Assess model bias, explainability, robustness, data lineage, and operational risk exposure
A: Risk Mgmt, Data Science
R: Data Science, Data Gov.
C: Risk, Legal, Procurement, IT, Data Governance
I: BoD, Internal Audit, Model Risk, Vendor Mgt, Internal Audit, HR
Model Development and Documentation (Model Risk Mgmt, MRM)
Develop models per governance standards, ensuring transparency, documentation and reproducibility
A: Data Science
R: IT, Data Gov.
C: Compliance, Model Risk, Vendor Mgt
I: BoD, Exec Mgmt, Internal Audit, HR/Training
C: MRM, Risk, IT, Business
I: BoD, Exec Mgmt, Internal Audit, Legal, Compliance, Vendor Mgt, HR/Training
Detailed AI RACI Matrix - Deployable Today
AI Activity
Independent Model Validation and Testing
Description and FINMA
Alignment
Primary Roles (A/R)
Supporting Roles (C/I)
Perform pre-deployement testing and ongoing validation to prevent bias and drift (as per FINMA (23/1).
A: Model Risk Mgmt
R: Risk
C: Compliance, Data Science
I: BoD, Exec Mgmt, Internal Audit
Approve “High-Risk”
AI Systems
Approve or reject “high-risk” AI systems (e.g., credit scoring, AML) per AI Act and FINMA oversight
A: BoD
R: Exec Mgmt
C: Business, Risk, Compliance, Model Risk, Legal, IT
I: Internal Audit, HR/Training Reporting
Identify, investigate, and remediate AI incidents or breaches
A: Exec Mgmt
R: Risk and Compliance
AI reporting
C: MRM, Risk, IT, Business, Data Science, Data Gov.
I: BoD, Internal Audit, Legal, Compliance, IT, Vendor Mgmt
Detailed AI RACI Matrix - Deployable Today
Operational Risk and Resilience AI Activity
Description and FINMA
Alignment
Procurement and Third-Party AI Providers (Vendor Management)
Manage AI-related operational and cybersecurity risks. Report findings to Mgmt and regulators
Primary Roles (A/R)
Supporting Roles (C/I)
A: Exec Mgmt
R: Compliance
C: Risk, Legal, Data Science, Risk, IT, Data Governance, Exec Mgmt
I: BoD, Internal Audit, Model Risk, Internal Audit, HR/Training
Outsourcing of AI systems, vendor due diligence, and SLA compliance. *Practical advice: negotiate compliant contracts
A: Exec Mgtm
R: Vendor Mgmt, IT
Deliver training to ensure AI literacy, governance, and align AI to risk exposure A: HR and IT R: Compliance
C: Compliance, Model Risk, I: Internal Audit, HR/Training
C: Risk, Data Science, Vendor Mgt
I: BoD, Exec Mgmt, Internal Audit, Legal
Detailed AI RACI Matrix - Deployable Today
From Practitioner to Practitioner:
“The regulatory authorities provide principle, directives, and guidelines, not rigid rulebooks. Within these frameworks, there is ample room for pragmatic solutions that both serve business needs and meet regulatory expectations. Yes, the volume of legal and compliance requirements can be overwhelming at first, it doesn’t have to paralyze progress. In my experience, a disciplined yet pragmatic approach, grounded in transparency and clear documentation consistently leads to successful outcomes. ”
Action Plan Checklist
Focus on revenue generating, high-value ROI use cases: advisory copilots, deal sourcing, and trading.
Appoint a QRF (Quick Reaction Force); a small and efficient cross functional team with the skills, experience and emotional intelligence to create trust and momentum fast. The results will speak for themselves. Establish or update a central AI inventory and classify applications by materiality (high-risk)
Assign accountable owners (RACI) for all material systems.
Enforce data quality and provenance standards across internal and vendor sources.
Implement a standard testing playbook: backtest, stress, adversarial. Put in place continuous performance and drift monitoring.
Document end-to-end model lifecycle, from purpose to fallback plan.
Commission independent reviews for all material AI systems. Review third-party contracts for visibility, audit rights, and data usage. Report consistently on compliance and business value
Leadership Challenge
From Insight to Action
The future of AI in banking will be written by the most intelligent and most disciplined AI adopters. Let’s build momentum that lasts.
If this resonates with you, I would welcome a conversation. The journey from concept to doing is never easy but ultimately rewarding.
Ethan B. Schaerer
Linkedin/Ethan_Schaerer
About the Author
Ethan Schaerer is a strategic thinker and a practitioner. He brings deep, hands-on industry experience across Switzerland, Europe, the United States, and Asia, combining strategic insight with practical leadership in highly regulated financial markets. He successfully led the acquisition of several cross-border banking and financial licenses, thus demonstrating a deep understanding of supervisory expectations, operating model design, and governance in complex international environments. He has held senior leadership roles driving high-profile regulatory and compliance programs for large global banks, as well as served as engagement leader for dozens of financial institutions in major regulatory reporting and governance initiatives. His experience spans front-office and sales leadership across Wealth Management, Asset Management, and Investment Banking, placing him in a unique position to integrate business growth with regulatory integrity.
Ethan Schaerer was hired by Jeff McDermott, former Co-Head of UBS Investment Bank, to co-build a leading, globally operating financial advisory firm that was later acquired by Nomura Investment Bank. From building businesses from scratch, to expanding products and services internationally, he developed deep expertise in scaling operations internationally, securing growth while maintaining operational integrity.
Today, he deploys that blend of strategic, regulatory, and commercial expertise in advancing financial institutions on their transformation journeys; helping boards and executive teams establish robust offerings, manage emerging risks responsibly, drive growth, and align technological innovation with the enduring principles of performance, trust, and accountability.
Disclaimer
This document was prepared with the purpose of serving as an informative and educational tool on issues of governance, risk, and compliance regarding AI in financial services. It encapsulates insights from extensive research into various regulatory frameworks, supervisory guidance, and industry-wide good practices and related reports (i.e., Deloitte AI in M&A 20254, 4Degrees.ai, McKinsey Banking AI Value 2025), along with professional experience from numerous discussions with regulatory bodies in different jurisdictions.
The rationale is mainly based, though not exclusively, on the following key regulatory sources:
FINMA Circular 23/1 - Operational Risks and Resilience
FINMA Circular 18/3 - Outsourcing
FINMA Circular 08/21 - Operational Risks - Banks
FINMA Circular 08/24 - Governance and Risk Management when using AI
Financial Services Act, FinSa
EU Artificial Intelligence Act (2024)
EU Digital Operational Resilience Act (DORA)
Guidelines by the European Banking Authority on Outsourcing Arrangements
European Central Bank (ECB) AI and Model Risk Guidance
BaFin Guidance on Artificial Intelligence and Algorithmic Systems
UK Financial Conduct Authority guidance on AI, Data and Algorithmic Trading, and other internationally based financial authorities such as FSA, APRA, etc.
These views also draw on Ethan Schaerer’s professional experience in managing regulatory and compliance programs and directly interacting with supervisory bodies on complex cross-border issues. These interactions have served to inform the practical interpretation and application of some of the frameworks discussed herein. While every effort has been made to ensure accuracy and consistency with current but evolving standards, this document does not constitute legal, regulatory, or supervisory advice. The reader should consider the relevance and applicability of the content in light of their own institutional structures, risk profiles, and jurisdictional requirements. All graphics and visual elements have been created using Canva for illustrative purposes only. Certain concepts and frameworks have been adapted or inspired by publicly available sources to support clarity and accessibility for banking practitioners.