From Stability to Continuous Adaptation

Asset and Liability Management (ALM) in banking has changed profoundly over the past two decades. What was once a relatively stable and internally focused discipline has become a central, forward-looking function shaped by regulation, market volatility, and technological change.

Before the Global Financial Crisis (2007/08), liquidity was rarely perceived as a binding constraint. Funding was broadly available, customer behavior was considered stable, and stress scenarios were often treated as remote. ALM frameworks reflected this environment – structured, but not necessarily designed for rapid shifts.

That perception no longer holds.

From regulatory response to structural change

The post-crisis regulatory framework fundamentally redefined how banks approach balance sheet management.

In liquidity risk, the introduction of the Liquidity Coverage Ratio (LCR) and Net Stable Funding Ratio (NSFR) established clear quantitative boundaries. At the same time, the Internal Liquidity Adequacy Assessment Process (ILAAP) brought a more comprehensive and bank-specific perspective.

ILAAP is not only a regulatory requirement; it is a structured process through which institutions identify, measure, manage, and monitor liquidity risk under EBA guidance. It must reflect the bank’s specific business model, size, complexity, and risk profile. Importantly, it requires a formal annual statement on liquidity adequacy, supported by analysis and approved by senior management, while assessing the bank’s ability to meet obligations under both normal and stressed conditions, including macroeconomic and idiosyncratic scenarios.

In parallel, the introduction of IRRBB (Interest Rate Risk in the Banking Book) added a new dimension to ALM. Banks are now required to assess interest rate risk using both earnings-based (NII) and economic value-based (EVE) perspectives. This dual view ensures that short-term profitability impacts and long-term balance sheet value sensitivities are both properly captured.

The IRRBB framework also formalized scenario design, stress testing, modelling assumptions, and risk identification across multiple dimensions, including yield curve risk, basis risk, optionality, and model risk. As a result, interest rate risk management has become significantly more structured - but also more demanding in terms of data, modelling, and interpretation.

Digitalization: speed as a new risk factor

While regulation reshaped the foundations of ALM, digitalization has introduced an additional layer of complexity - primarily through speed.

Instant payments, mobile banking, and trading platforms have changed how quickly liquidity can move. Deposits that were once considered stable can now be transferred within seconds. This has direct implications for liquidity buffers, stress assumptions, and behavioral modelling.

In particular, non-maturity deposits (NMDs) have become a key focus area. Traditional modelling approaches are being revisited, as banks need to better capture behavioral patterns, segmentation (e.g. insured vs. uninsured deposits), and sensitivity to market conditions.

A function under pressure: new questions for ALM

These developments have led to a shift in the types of questions ALM teams must address.

The calibration of FTP curves is no longer a purely technical exercise - it directly affects pricing, profitability, and strategic steering. Financial planning must be closely aligned with balance sheet dynamics, rather than treated as a separate process.

At the same time, recent market events have raised important questions about liquidity usability. Holding sufficient high-quality liquid assets (HQLA) does not automatically guarantee their effective usage under stress. The experience of rapid deposit outflows in certain banks (such as SVB in 2023) has highlighted this gap between theoretical and practical liquidity.

There is also an increasing discussion around whether current regulatory metrics are sufficient, or whether shorter-term liquidity measures could emerge in the future.

The evolving role of ALM

In this environment, ALM is no longer a backward-looking control function. It is becoming an active, decision-support framework that must operate with both speed and depth. This means being able to simulate multiple scenarios quickly and provide management with a clear view of potential impacts on the balance sheet and profitability. It also requires a strong understanding of market access - how and when liquidity can be obtained, including through central bank facilities.

Equally important is the ability to model customer behavior realistically, particularly in areas where assumptions have historically been simplified. What is emerging is a more integrated view of ALM, Treasury, Risk, and Finance - where decisions are interconnected and timing matters.

Final thought

The environment in which banks operate has become more volatile, more transparent, and significantly faster. ALM sits at the centre of this transformation. Its role is no longer limited to measurement and compliance - it is increasingly about anticipation, interpretation, and strategic guidance.

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