Risk, Regulation, and Trust: Why Data Governance Defines Leadership in Banking and FinTech

Key Insights: 

• Risk in banking and FinTech is now a data problem. Credit, fraud, liquidity, and AI-driven decisions depend on the quality, governance, and interpretability of data, not just capital or controls. 
• Banks and FinTechs share the same trust obligation. As digital operating models converge, transparency, accountability, and responsible data use define institutional credibility. 
• Weak data governance magnifies uncertainty. In volatile, fast-changing economic and geopolitical conditions, unreliable data limits an institution’s ability to respond, pivot, or manage risk.  
• Regulators now expect evidence, not intent. Supervisory focus is shifting toward demonstrable data lineage, explainable models, and embedded governance across digital platforms. 
• Data governance is an executive leadership differentiator. Institutions that treat governance as a strategic responsibility move faster, earn trust, and innovate with confidence. 

Macro and Micro Views on the Use of Data in Financial Services 

In past articles, the views and observations expressed were primarily from a micro-level perspective on the use of data within a specific domain. It could be argued that the datasets reviewed are tactical, addressing specific business processes. [1, 2]  

This article shifts the focus to a broader picture – how changes in the world economy and political and economic shifts will make data significantly more important for solving strategic challenges.  

Modern data science is built on the power of big data, and as I pointed out, it is easy to get lost in the sea of data; however, being lost in a sea of data does not mean we can’t attempt to identify patterns and trends in the information. The financial services industry is changing, and the pace of change is accelerating.  

The first FinTech, Security First Bank, was created in 1995 and sparked recognition that financial services could be improved through technology. [3]  Innovations introduced by FinTechs  greatly influenced traditional banks and forced them to adopt new technology to remain competitive. 

Two Models, One Trust Imperative 

Traditional banks and FinTech companies often view themselves as fundamentally different. Banks emphasize stability, regulatory compliance, and balance sheet strength. FinTechs emphasize speed, innovation, customer experience, and platform-driven growth. Yet beneath these differences lies a shared reality: both are now data-centric financial institutions and depend on trust built through responsible data use. 

Since then, digital channels, real-time payments, embedded finance, and AI-driven decisioning have become ubiquitous, and the distinction between banks and FinTechs continues to blur. Regulators increasingly treat FinTechs as systemically relevant financial actors, while banks adopt FinTech-style architectures and operating models. In this converging landscape, risk, regulation, and trust are no longer institution-specific concerns but are data-driven imperatives. 

Risk Is No Longer a Balance Sheet Issue Alone 

For decades, banks managed risk primarily through capital adequacy, credit underwriting discipline, and market exposure controls. FinTechs, by contrast, often framed risk in terms of platform reliability, fraud prevention, and growth sustainability. Today, both models face the same underlying truth: risk has become a data problem. 

Data Quality and Interpretation 

Poor data quality can distort credit models, increase false positives or negatives in fraud detection, undermine stress testing, and lead to unfair or non-compliant customer outcomes. As automation accelerates, errors propagate faster and farther. 

New Pressures 

As capital fractures amid geopolitical tensions (trade and kinetic conflicts), technological advancements, and demographic trends, global commerce and financial services business models must adapt. Not only are there more variables in play than usual, but the sector’s pace of change (adaptation) is accelerating at a speed never imagined. Executives are trying to navigate the complexities of a multi-shock world, where the only thing firms can be certain of is uncertainty. World events are rapidly reshaping the financial services landscape; resilient companies will focus on having the data, analytical capability, and leadership to pivot toward opportunity and away from uncontrolled risk. [4] 

This is not a new problem. Information has always been instrumental in decision-making. Richard Hamming (1915-1998) is credited with coining the phrase “You get what you measure.”  [5, 6] 

However, bias can deceive us, allowing us to see things as we want to, rather than as they really are.  We may think our measurements are accurate, but personal desires cloud the analysis, and we create false narratives.  

The 2008 Financial Crisis 

The 2008 global financial crisis was the worst economic disaster since the Great Depression. It caused upheaval in financial markets worldwide, brought down major banks, and left millions of people without homes, jobs, or savings. At its core, the crisis stemmed from a toxic mix of deregulation, excessive risk-taking, lax lending standards, and the bursting of a massive housing bubble. But the seeds of the crash were sown over many years through flawed policy decisions and unchecked market excesses. 

One of the most significant lessons learned from the global financial crisis that began in 2007 was that banks’ information technology (IT) and data architectures were inadequate to support the broad management of financial risks. Many banks could not aggregate risk exposures and quickly and accurately identify concentrations at the bank group level, across business lines, and between legal entities. Some banks were unable to manage their risks effectively due to weak risk data aggregation capabilities and risk reporting practices. This had severe consequences for the banks themselves and for the stability of the financial system overall. [7] 

The data had been there for some time, and a time bomb and an equivalent financial time bomb had been set, with the fuse lit. As Michael Burry observed in a New York Times Op-Ed article written after the crisis, many formerly prestigious firms (Lehman Brothers, Bear Stearns, and others) collapsed. Several factors caused the crash. [8] 

In 2008, after the crash, Alan Greenspan, the former chairman of the Federal Reserve, admitted he had made a mistake.  He is quoted as saying: 

“I made a mistake in presuming that the self-interest of organizations, specifically banks and others, was such that they were best capable of protecting their own shareholders,” he said. [9] 

Regulation, Innovation, and Uncertainty 

Economists widely expect that the extensive use of leverage across the financial system, combined with policy efforts to lower interest rates in support of economic growth, will increase inflationary pressures. For banks, FinTechs, and investors, this environment heightens uncertainty about asset valuations, funding costs, and long-term capital allocation. At the same time, regulators are struggling to keep up with the rapid pace of change in financial services, while market indicators suggest that sophisticated institutional investors are increasingly diversifying exposure away from the U.S. in search of more stable or higher-growth opportunities abroad. [4] 

Regulatory focus is intensifying around data lineage, technology controls, and algorithmic decision-making—areas at the core of modern FinTech and digital banking models. As a result, FinTech firms are facing bank-like regulatory expectations much earlier in their growth cycles, particularly in domains such as consumer protection, operational resilience, model governance, and data management. For investors, this shift materially alters risk profiles, compliance costs, and time-to-scale assumptions, while for institutions, it reinforces the need to embed regulatory readiness in product and platform design from the outset. [10] 

The financial services sector is entering a period of meaningful regulatory realignment. Shifting political priorities, macroeconomic volatility, and rapid technological innovation are prompting regulators worldwide to reassess how financial oversight should be applied in a digital-first economy. In the United States, agencies including the Consumer Financial Protection Bureau (CFPB), the Office of the Comptroller of the Currency (OCC), and the Federal Deposit Insurance Corporation (FDIC) are evolving their supervisory approaches to address emerging risks associated with non-bank financial institutions, embedded finance, and technology-driven business models. For banks and FinTechs alike, regulatory engagement is increasingly a strategic function rather than a purely compliance-driven exercise. [4] 

At the same time, digital assets and cryptocurrency markets are shifting from speculative experimentation toward regulated financial infrastructure. The emergence of central bank digital currencies (CBDCs), growing institutional participation, and increased regulatory scrutiny are redefining how digital assets fit within the broader financial ecosystem. For investors and financial institutions, this evolution presents both opportunity and risk: blockchain-enabled settlement, tokenized assets, and programmable money offer efficiency gains and new revenue models, while regulatory uncertainty and jurisdictional fragmentation remain key concerns. The next five years will be decisive as policymakers, market participants, and investors seek to balance innovation with regulatory discipline—determining whether digital assets become a catalyst for financial system modernization or a constrained niche within it. [11] 

Explainability as a Trust Requirement 

To maintain customer trust, institutions must clearly explain how data-driven decisions are made. Explainability is no longer optional—it is a regulatory, legal, and reputational imperative. Regulators and customers will demand it as a key performance indicator. Customers choose providers based on transparency, fairness, and data stewardship. Trust increasingly determines market success. 

The Cost of Weak Data Governance 

Weak governance leads to regulatory fines, customer attrition, operational inefficiencies, and strategic hesitation. Strong governance enables speed, innovation, and trust.  

Executive Accountability 

Data leadership is executive leadership. CIOs, CTOs, CISOs, CROs, and CFOs must align governance with a clear-sighted assessment of what the data shows, so that it supports the institution’s strategy and maintains customer trust and regulatory compliance. A response from the executive suite that there was “no way of knowing” in the event of a major market correction will not suffice. 

Conclusion 

Data unifies risk, regulation, and trust. Institutions that master data governance will lead in banking and FinTech over the next decade. Green Leaf has data experts and C-level fractional executives on staff who can develop a cost-effective, practical data governance program that addresses regulatory and operational risk and provides proof points for clients and their customers that they take data protection, privacy, and innovation seriously. If you have these concerns and would like an objective review, please contact us.

References 

1. Ferrara, E., Data in the Evolving World of Life Sciences_ Chaos to Order, in Green Leaf Consulting Group – Insights, M. Miner, Editor. 2025, Greenleaf Group: https://greenleafgrp.com/insights/data-in-the-evolving-world-of-life-sciences-chaos-to-order/. 
2. Ferrara, E., Why Data Now Defines Value in Banking and Financial Services, in Green Leaf Consulting Group Insights, M. Miner, Editor. 2025, Greenleaf Group: https://greenleafgrp.com/insights/why-data-now-defines-value-in-banking-and-financial-services/. 
3. Wikipedia, Security First Network Bank. 2025.
4. Peter, P., What will be left of financial services tomorrow?, in PWC – Insights. 2025, PWC: https://www.pwc.com/us/en/industries/assets/industry-edge-financial-services-tomorrow.pdf. 
5. Ferrara, E., Data in the Evolving World of Life Science (Part 2), in Green Leaf Consulting Group – Insights, M. Miner, Editor. 2025, Greenleaf Group: https://greenleafgrp.com/insights/data-in-the-evolving-world-of-life-sciences-part-2/. 
6. Hamming, R.W., Hamming, “You Get What You Measure” (June 1, 1995). 1995: YouTube.
7. Adachi, M., et al., Principles for effective risk data aggregation and risk reporting, F. Vargas, Editor. 2013, Bank for International Settlements: Basel, Switzerland.
8. Burry, M., I Saw the Crisis Coming. Why Didn’t the Fed?, in The New York Times. 2010, The New York Times: New York, NY, USA. 
9. Beattie, A. and J. Politi,‘I made a mistake,’ admits Greenspan, inFinancial Times. 2008, Financial Times: London, UK. 
10. Sheth, N., Fintech’s Next Big Challenge? Thriving In An Era Of Regulatory Uncertainty, in Forbes. 2025, Forbes Media: New York, NY, USA. 
11. Kumar, U., 7 Trends of AI and Data Science in 2026.Medium, 2026.