The stability and success of any financial institution hinge on effective Asset-Liability Management (ALM). Following the banking turmoil in 2023, it has become clear that robust ALM practices are no longer a luxury but a necessity for managing key risks — liquidity, profitability, and regulatory compliance.

Professional ALM provides the foundation for accurate forecasting and informed decision-making.

The stability of a bank is intrinsically linked to how it manages Non-Maturing Deposits (NMDs). These deposits are contractually floating-rate liabilities with zero maturity, allowing immediate withdrawal. However, NMDs typically exhibit “stickiness” and remain for extended periods, making them a crucial — yet unpredictable — funding source for banks.

Since the 2007 global financial crisis, the importance of correct NMD modeling has only grown. Inappropriate behavioral assumptions can underestimate risks or even lead to “window-dressing” strategies that mask maturity mismatches — a genuine concern for financial stability.

1️⃣ Mapping Risk Positions: The Foundation of ALM

The backbone of a resilient ALM strategy is quantitative planning, which uses data-driven tools to project future cash flows, interest income, expenses, and liquidity needs.

A comprehensive ALM strategy must incorporate Interest Rate Risk (IRR) management, liquidity management, and credit-spread risk management.

A crucial element in this process is identifying and mapping both liquidity (LQ) and interest rate commitment (IR) for products without a fixed maturity (NMD/NMA). Accurate mapping allows for effective IRR management, which is essential for optimizing Net Interest Income (NII).

2️⃣ The Critical Challenge: Modeling Non-Maturing Products

Non-Maturing Deposits (NMDs), such as sight deposits and transactional current accounts, are contractually defined as floating-rate liabilities with zero maturity. Yet, in reality, they behave differently — they remain for extended periods.

To map these products correctly, banks must use internal models based on historical customer behavior to estimate deposit stability and determine their behavioral maturity.

Supervisory data show large differences between contractual and behavioral maturities assumed by banks. Under behavioral assumptions, only about 20% of NMDs are treated as liabilities with zero maturity, while around 10% are assigned maturities exceeding seven years — and roughly 1.5% even above 15 years. Some banks therefore consider a significant portion of their deposits as highly stable.

A robust NMD model should consider:

• Long-term historical product volumes and interest rates (ideally 10+ years of monthly data).
• Liquidity premiums and risk-free market rates (ideally 20-year history).
• Client behavioral features, such as early withdrawals, prepayments (on the asset side), early repricing, and volatility.

The goal of this modeling (for example, using the ALM_IRLQ model) is to define maturity profiles and assign the corresponding internal interest rate (FTP IR) and liquidity premium or cost (FTP LQ). The final decision on volume distribution in the replication portfolio rests with the responsible ALM manager.

3️⃣ From Mapped Positions to Strategic Decisions

Once the liquidity and interest rate positions are mapped, and NMDs have been assigned virtual maturities and Funds Transfer Prices (FTPs), bank decision-makers and the ALCO committee can take strategic action.

The FTP system calculates how individual assets and liabilities contribute to the bank's overall profitability. It must assign a benchmark price (FTP) that corresponds to a feasible, risk-free market rate linked to the product’s maturity or next repricing.

The total FTP typically consists of:

• Basic FTP – reflecting interest rate risk, and
• Liquidity premium (FTP LQ) – capturing liquidity risk (aka "liquidity premium")
• Other FTP components (Contingency spread, Credit-risk volatility spread, Option Spread, Basis Spread, Bonus/Malus)

Based on these calculated metrics, management can make key strategic decisions, including:

• Adjusting client interest rates or FTP components,
• Executing bond purchases or sales to reshape the balance sheet,
• Or fine-tuning the structure of product portfolios to stabilize margins.

Ultimately, the objective of interest replication portfolio modeling is to find an FTP setup that ensures stable product margins over time — a cornerstone of sustainable bank performance.

4️⃣ Necessities for Professional ALM

Achieving professional and resilient ALM requires a combination of capabilities and support:

1. Know-how and Expertise of ALM Staff: ALM professionals must possess strong know-how and experience. Continuous training and consulting are essential to keep up with developments in ALM, Treasury, and market risk management.
2. Appropriate Tools: Manual tools like spreadsheets are insufficient for handling complex datasets and detailed scenario analyses. Professional ALM requires quantitative tools such as: (a) QuantPlan – for balance sheet projection, quantitative planning, and scenario analysis, (b) ALM_IRLQ – a Bearning Excel-based model for non-maturing product replication and FTP modeling. Modern systems should support stress-testing to assess balance sheet resilience against rate shocks or behavioral changes.
3. Access to Capital Markets: Banks need capital market access to manage risks via instruments such as bonds, repos, and derivatives like Interest Rate Swaps (IRS). A comprehensive FTP system can also integrate the effects of credit spread volatility (FTP-CS) and option risk (FTP-O).
4. Support from Decision-Makers and Shareholders: Strong ALM depends on active involvement from bank management and the ALCO committee. Quantitative planning enables informed decision-making — helping managers forecast accurately, manage risk, and capture business opportunities while aligning with shareholder return expectations.

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