• AI is helping banks transform trusted balance sheet data into faster, more informed decisions.
• A strong data foundation is essential for successful AI adoption and regulatory confidence.
• AI delivers the greatest value in pattern recognition, decision support and balance sheet optimisation.
• Explainability, auditability and clear data lineage are critical for AI in regulated environments.
• Treasury, finance and risk teams should use AI to augment expert judgement, not replace it.
• Institutions that combine trusted data, strong governance and AI-driven decision support will be best positioned to manage future balance sheet risk.

As banks navigate increasingly complex balance sheets, volatile market conditions, and evolving regulatory expectations, the ability to transform data into timely, actionable insight has become a strategic imperative. Surya FinTech, a specialist provider of balance sheet management technology with over 26 years of experience across more than 20 countries, shares its perspective on where AI is delivering measurable value, how trusted data enables effective AI adoption, and how financial institutions can apply AI responsibly to support balance sheet decision-making.

Q1. What is the core challenge banks face in turning regulatory data into actionable insight?

We work with banks across 20+ countries, serving institutions of varying sizes and organizational structures. Across these institutions, the most consistent barrier is not a shortage of data, but its complexity. For some banks, the complexity stems from the sheer scale of data; for others, it comes from data being fragmented across core banking, treasury, risk, finance, and regulatory systems, each operating on different timelines, formats, and definitions. Primary Reason to build our Data Harmoniser platform is to address these challenges by rapidly processing and reconciling data from disparate sources into a single, trusted view of the balance sheet. This not only eliminates the overhead of manual aggregation but also ensures that analytics and AI operate on timely, consistent, and reliable data, enabling faster and more informed decision-making.

Q2. Where does AI genuinely add value in balance sheet management, and where are the limits?

What we see working well in practice is AI applied to pattern recognition across large, structured balance sheet datasets-identifying rate sensitivity concentrations, flagging liquidity mismatches, and surfacing early signals that would otherwise take analysts days to find. Through Surya.AI, we have focused on enabling natural language interaction with balance sheet data, allowing ALM and treasury teams to ask questions directly and receive explainable answers without depending on technical specialists for every analysis. Beyond insights, we are also applying AI to decision support and optimization use cases, such as HQLA optimization, helping institutions evaluate alternative strategies while balancing profitability, liquidity, and regulatory constraints. Where AI has clear limits is in regulatory judgment and contextual interpretation - it can surface the signal and evaluate possible actions, but the senior professional must still own this decision. The institutions extracting the most value are those treating AI as an accelerant to expertise, not a replacement for it.

Q3. How should banks approach explainability when deploying AI in regulated risk functions?

In our experience, explainability cannot be retrofitted - it must be designed into the data lineage from the outset. When we build AI-powered outputs within our platform, every result is traceable to a reconciled source, with full auditability from the output back to the underlying transaction level. Regulators and internal audit teams increasingly require institutions to explain not just what a model concluded, but the data chain that produced that conclusion - and that is a data governance problem as much as a model transparency problem. We have found that institutions with robust data infrastructure can deploy AI responsibly in regulated contexts; those without it face a credibility challenge regardless of the sophistication of the model itself.

Q4. How is AI changing the day-to-day work of risk and finance teams?

The consistent feedback from risk, treasury, and finance teams is that AI is beginning to shift the focus from routine data preparation towards higher-value analysis and decision support. While adoption is still at an early stage for many institutions, teams are actively exploring where AI can deliver the greatest value-whether in accelerating data analysis, improving access to information through natural language interfaces, identifying emerging risk patterns, or supporting optimization decisions. AI is not a silver bullet. It depends on trusted data, strong governance, and clear business objectives, and its recommendations still require expert validation. The institutions seeing the greatest success are those taking a pragmatic approach-starting with well-defined use cases and using AI to augment experienced professionals rather than replace them. 

Q5. What will distinguish institutions that use AI effectively in balance sheet risk management over the next five years?

From our perspective, the differentiator over the next five years will not simply be access to AI, but the ability to apply it effectively to balance sheet management. Institutions that combine trusted data, proven balance sheet management capabilities, and AI-driven decision support will be better positioned to respond to changing market conditions, evaluate trade-offs, and make faster, better-informed decisions. AI will evolve from answering questions to recommending actions, optimizing balance sheet strategies, and identifying emerging risks and opportunities. Its greatest value, however, will come from augmenting the expertise of treasury, finance, and risk professionals-not replacing their judgment.