Why Structured and Accessible Regulatory Data Is Foundational for AI-Driven Finance
Credit to Max Gerrard, Senior Solutions Consultant at Suade
Executive Summary
Banks are increasingly turning to artificial intelligence to improve decision-making across finance, risk, and compliance. Yet despite significant investment, many AI initiatives struggle to deliver reliable and auditable results. This is less about the sophistication of the models and more about the state of the underlying regulatory data. When regulatory data is fragmented, inconsistently defined, or difficult to trace, AI amplifies those weaknesses rather than correcting them.
For CFOs, this is not just a technology issue, it is a question of financial control, capital efficiency, and accountability. Without well-structured and accessible regulatory data, AI-driven analysis can increase model risk, audit exposure, and produce overly conservative capital outcomes driven by uncertainty rather than actual risk. This paper explores why regulatory data should be treated as a first-line financial control, how AI changes the risk profile of weak data foundations, and what CFOs can do to ensure AI strengthens rather than undermines regulatory confidence and performance.
The CFO’s Expanding Accountability
Over the past decade, the responsibilities of the CFO have expanded far beyond traditional financial reporting. Today, CFOs are accountable not only for regulatory capital and liquidity outcomes, but also for stress testing, scenario analysis, the integrity of data used for management and board decisions, and the effectiveness of audit remediation and internal controls. Regulatory data now sits squarely at the intersection of finance, risk, and compliance.
Yet in many banks, this data remains scattered across multiple systems, lacks uniform definitions across teams, is heavily adjusted through manual processes, and difficult to reconcile. This fragmented approach embeds uncertainty into financial decision-making and limits the organisation’s capacity for strategic action.
Regulatory Data as a Financial Control Asset
CFOs understand the importance of controls over financial reporting, and regulatory data deserves the same treatment. When well-structured, regulatory data serves as a control for reporting accuracy, a critical asset for capital allocation, a foundation for credible stress testing, and tangible evidence of governance for regulators and auditors.
Conversely, when regulatory data is inaccessible or poorly organized, controls become procedural rather than substantive. Manual checks replace auditable data foundations, and confidence in decision-making erodes. Regulatory data quality should not be treated as an IT concern but a financial control, owned by the CFO and executed through technology.
AI as a Risk Multiplier
AI is often marketed as a solution to complexity, but in regulatory contexts, this is a dangerous assumption. AI systems learn patterns from historical data, generate outputs with certain confidence levels, and can scale quickly across multiple use cases. If the underlying data is inconsistently defined, poorly documented, or lacks clear lineage, AI merely industrialises existing weaknesses.
In practice, this can mean drawing inaccurate conclusions, generating outputs that cannot be audited, increasing model risk and eroding trust with both regulators and internal stakeholders. AI does not fix poor data; it magnifies its flaws.
The Cost of Unstructured Regulatory Data
The financial consequences of weak regulatory data are both material and ongoing. Compliance becomes costlier as data preparation is duplicated across Finance, Risk, and Compliance teams. Manual reconciliations increase, and audits take longer, requiring more extensive remediation programs.
Capital and liquidity decisions are also affected. Data uncertainty often leads to conservative capital buffers, limits the ability to explain scenario outcomes, and reduces confidence in capital optimisation decisions. Control and defensibility risks emerge as outputs cannot be traced back to their source, answers vary by team or methodology, and regulatory responses are slower and less credible. In regulatory contexts, this uncertainty carries a tangible cost, often reflected in higher CET1 capital requirements.
Defining “Well-Structured and Accessible” Data
Well-structured regulatory data is defined by outcomes rather than technology. It requires common definitions applied consistently across functions, clear data lineage from source to regulatory output, standardised calculation logic, and governed access with accountability. The data must be usable by both humans and machines.
Accessibility does not mean unrestricted access; it means controlled, explainable, and scalable access that enables effective decision-making while maintaining governance standards.
From Manual Effort to Analytical Confidence
Traditionally, regulatory data has been scattered across siloed systems and heavily reliant on spreadsheets and manual adjustments. Results often vary depending on who runs the analysis. AI outputs in such an environment are difficult to explain or defend.
With a governed data foundation, organisations can reconcile sources, standardise definitions, and leverage AI for insight rather than data wrangling. Outputs become explainable, auditable, and repeatable. The shift is less about adopting new technology and more about moving from a legacy approach to one that is future-proof.
Why CFO Leadership Matters
Technology teams can build platforms, but they cannot define financial accountability. A CFO-led approach ensures alignment with financial controls and governance. Establishing clear ownership of definitions and outcomes, fosters strong collaboration between Finance, Risk, and Technology, and builds credibility with regulators and auditors. This effort is not about creating a data lake; it is about cultivating a culture of regulatory truth.
The Defining Question for CFOs
As firms adopt AI for regulatory analysis, the critical question is no longer whether AI can deliver timely, sophisticated insights. It’s whether your AI-driven insights of tomorrow are validated by traceable data and explainable logic. Can you confidently stand behind the result?
Conclusion
AI has the potential to transform regulatory analysis, but only for institutions with regulatory data foundations robust enough to support it. For CFOs, investing in well-structured and accessible regulatory data is not an innovation bet, it is a control decision, a capital decision, and a leadership decision. AI does not create regulatory insight; it reveals whether your regulatory data can be relied on.