Rethinking Bank ALM as a Strategic Decision

In today’s banking landscape, asset/liability management (ALM) is too often seen solely as a risk mitigation function. While managing risk is critical — especially considering events like the Silicon Valley Bank failure — the true essence of excellent banking lies in balancing risk with margin enhancement. This challenge should be at the heart of every asset/liability committee (ALCO).

As interest rate projections remain uncertain due to changing economic factors, many banks are unprepared for a volatile environment. A June 2024 McKinsey study noted that many institutions “lack the necessary playbook” to adapt, with smaller banks suffering additional interest expenses nearly three times higher than the top-performing institutions.

Rethinking ALM’s Role
At their core, banks engage in maturity risk transformation to generate profit margins. Top-performing banks recognize that this means continuously evaluating trade-offs between risk and return in every decision. For example, implementing funds transfer pricing can be valuable by allowing the loan business line to book expected profits for each loan issued above the treasury hurdle rate and similarly for the deposit business.

Besides assessing the expected profits, banks can and should monitor the incremental interest rate and liquidity risks associated with new loans. It is essential to assess whether a particular loan exacerbates or mitigates the bank’s aggregate risk exposure to determine if the anticipated profits justify the risks.

While loan, securities and deposit teams work separately, the ALCO often meets periodically to assess the balance sheet’s sensitivity. ALCOs often rely on simplistic parallel rate shocks, unrealistic stress tests and arbitrary risk limits. The tools used by chief loan and deposit officers are isolated from the broader ALCO strategy and run in their own data silos.

This reactive and siloed approach risks significant long-term opportunities. Volatile markets are bound to deliver unexpected shocks, and unforeseen events are inevitable. Rather than retreating into defensive postures, banks must leverage new technologies, software and insights to turn risk into reward.

Leveraging Machine Learning
Bank leaders must reassess how to evaluate new loans. It is not enough to consider the profit potential, so institutions must also gauge the incremental interest rate and liquidity risks a loan introduces. Every lending decision shapes a bank’s overall risk profile — choosing inaction is a decision, too.

Historically, only large banks could afford sophisticated financial models. Today, advanced analytics, machine-learning tools and real-time risk assessments are accessible to institutions of all sizes.

Consider three fictional $1 billion banks contemplating a $300 million commercial loan linked to the prime rate, priced at 8.5%. At first glance, the loan promises a strong boost to net interest margins (NIM) based on typical funding costs of 2.5%. Yet, because the loan would comprise a significant portion of the balance sheet, the bank must weigh potential risks and rewards.

The first bank might approve the loan without sufficient risk analysis, hoping for favorable rates. But if rates drop unexpectedly, the anticipated NIM boost could vanish, prompting tough questions from the bank’s board.

The second bank, operating with too much caution, might find that a plus-or-minus 400-point parallel rate shock could swing projected NIMs by an unacceptable margin. But they might later realize that their balance sheet was liability-sensitive, and this loan could have reduced overall NIM volatility.

The third bank, equipped with real-time, AI-driven analytics, would quickly assess the risk and quickly and safely implement a rudimentary swap to lock in the profits while mitigating risk.

AI systems developed under rigorous, stress-tested conditions offer real-time insights and strategic options to banks of all sizes. With these tools, bankers can simulate multiple scenarios rapidly, transforming risk management from a defensive chore into a proactive strategy for growth.

The current climate of uncertainty gives community banks a chance to reorient their strategies and capture value. Advanced analytics allow banks to:

• Automatically price and integrate interest rate swaps into loan offerings.
• Reallocate exposure across loans and deposits to seize profitable opportunities.
• Construct synthetic swaps using futures contracts.

These measures represent proactive strategies to boost profitability and stabilize the balance sheet amid market turbulence.

In a volatile market, banks can no longer afford to treat ALM as an administrative burden. Leading financial institutions must embrace advanced tools and integrate risk management for return optimization, allowing institutions to protect their portfolios, uncover new revenue streams and gain a competitive edge.

ALM will remain a critical skill as the banking environment continues to shift, and leveraging next-generation analytics is essential for banks that want to thrive in a rapidly changing world.