How AI Is Transforming Balance Sheet Management

Ahead of Balance Sheet Management Europe, we spoke with Massimo Pedroni, Head of International Business for the Enterprise Risk Management Area at Prometeia, about how ALM is evolving into a strategic balance sheet management capability. He discusses why integrating risk, liquidity, capital, funding, and profitability is becoming essential, how AI and advanced analytics are improving decision-making, and what will distinguish leading balance sheet management functions as financial institutions navigate increasing complexity and market uncertainty.

What are the must-have characteristics for a successful ALM function in the modern world, and how are leading institutions using technology to move beyond traditional balance sheet reporting toward more strategic decision-making?

A successful modern ALM function integrates risk, liquidity, capital, and profitability management while providing accurate, forward-looking insights for decision-making. Leading institutions take advantage of cloud platforms, end-to-end automation, advanced analytics, and AI to enable real-time scenario analysis and improve forecasting. This shifts ALM from traditional balance sheet reporting to a strategic tool that supports business growth, risk management, and balance sheet optimization.

What are the best practices organisations should be adopting to bring together risk, funding, liquidity, capital, and profitability insights into a single, integrated ALM framework?

Organizations should adopt integrated ALM platforms with a single source of high-quality data, consistent methodological assumptions, and strong Model Risk governance across finance, treasury, and risk functions. Best practices also include automating FTP and Hedge Accounting cycles, with the aim of actively steering future P&L, and performing regular scenario and stress testing. This enables more informed strategic decisions by linking risk, funding, liquidity, capital, and profitability in one framework, in compliance with BCBS239 standards

How can firms ensure that advances in data, analytics, and ALM platforms translate into faster and more effective business decisions rather than simply generating more information?

Firms should focus on delivering clear, actionable insights through ALCO dashboards, automated reporting, and scenario analysis rather than simply producing regulatory indicators. Integrating ALM analytics into strategic planning and decision-making processes ensures insights are used effectively. Strong Model Risk governance and collaboration across finance, treasury, and risk teams also help turn information into timely business decisions.

Looking ahead, where do you see AI delivering the greatest value within ALM, and which use cases are most likely to transform how balance sheet decisions are made?

AI is expected to deliver the greatest value in behavioral modelling, scenario analysis, and balance sheet optimization by improving the accuracy and speed of decision-making. Key use cases include transactional data modelling to predict depositors’ behavior, optimizing prepayment risk management, and identifying emerging risks in real time. These capabilities will enable ALM teams to make more proactive, data-driven strategic decisions rather than relying on historical analysis.

As technology continues to evolve, what will distinguish a best-in-class ALM function over the next five years, and how will the roles of treasury, finance, and risk teams need to adapt?

Over the next five years, best-in-class ALM functions will be distinguished by AI-driven capabilities, large use of advanced analytics, and fully integrated decision-making across treasury, finance, and risk. These teams will need to work more collaboratively, using shared data and models to optimize balance sheet performance and respond quickly to changing market conditions and geopolitical scenarios. The focus will shift from producing reports to delivering strategic insights that drive business value.