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Asset-liability management (ALM) is a critical activity for banks — not just for meeting regulators’ expectations, but also as a strategic tool for controlling risk and enhancing performance. In today’s high-risk environment, it’s critical for a bank’s board of directors or asset and liability committee to take a proactive approach to ALM. There are a variety of ALM modeling software programs available, ranging from simple spreadsheets to outsourced solutions and sophisticated, highly customized in-house systems. Here are some questions to ask as you evaluate your bank’s program.

What’s your bank’s risk profile?

The right ALM system for your bank depends on the complexity of your balance sheet and the degree of risk to which it’s exposed. Nearly every balance sheet includes some level of complex financial products and instruments where simulation modeling is needed. These products and instruments typically contain embedded options — such as prepayment rights for loans or securities and early withdrawal options on deposits.

There might also be put or call provisions, caps and floors, or conversion rights on investments or alternative funding sources. Evaluating the risks these options present requires detailed assumptions about future interest rates, economic conditions and customer and investor behavior.

Is your model dynamic?

Static simulation models are simpler but of limited value because they assume a constant balance sheet without new growth. More sophisticated dynamic models incorporate assumptions about potential new business and changes in business lines. Again, the level of sophistication your bank requires depends on its risk profile.

A model is only as good as its assumptions. A dynamic model will include assumptions about the behavior of your bank’s current business as well as assumptions about future business. Where does this data come from? Some models incorporate actual details about your portfolio, while others rely on aggregate data.

Actual details should be imported directly into the program to avoid errors. The level of sophistication your bank requires in a simulation model depends on its risk profile.

What about liquidity risk?

Traditionally, ALM models have focused on interest rate risk, but in recent years liquidity risk has emerged as a significant financial risk. Much of the data captured by ALM models can be useful in managing liquidity risk, but few banks take full advantage of these capabilities. Check to see if your ALM software can measure liquidity risk or export the necessary data to a liquidity modeling program.

Managing risk

ALM modeling allows you to measure your bank’s risks and to take action — such as reducing your exposure or increasing your capital — if those risks are unacceptably high. The type of model and level of sophistication that’s right for your bank depends on its size, complexity, business model, risk profile and other characteristics.

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