Banks model behavioral assumptions for IRRBB calculations by estimating how customers will actually behave with their financial products, rather than relying on contractual terms alone. This means modeling when depositors will withdraw funds, when borrowers will repay early, and how rate-sensitive each product group is. The sections below walk through the main behavioral assumption types, how they are estimated and validated, and how regulators assess them.

What types of customer behaviors do banks need to model for IRRBB?

For IRRBB calculations, banks need to model three main categories of customer behavior: deposit repricing and withdrawal patterns, loan prepayment rates, and the exercise of embedded optionality in financial products. These behaviors determine how quickly a bank’s balance sheet reprices when interest rates move, which directly drives both Economic Value of Equity (EVE) and Net Interest Income (NII) sensitivity.

Contractual terms rarely reflect what customers actually do. A sight deposit technically matures overnight, but in practice a large portion stays in the account for years. A fixed-rate mortgage has a defined term, but many borrowers refinance early when rates fall. If banks used contractual maturities alone, they would significantly misstate their interest rate risk position.

The main behavioral categories banks model include:

• Non-maturity deposits (NMDs): current accounts, savings accounts, and demand deposits with no fixed maturity date
• Loan prepayments: early repayment of fixed-rate loans, mortgages, and consumer credit
• Loan commitments and drawdown behavior: how quickly undrawn credit facilities are utilized
• Pipeline and new business assumptions: expected volume and pricing of new originations under different rate scenarios

Each of these behaviors introduces optionality into the balance sheet. The customer holds the option to act in a way that benefits them, and the bank needs to price and hedge that risk accordingly under the IRRBB framework.

How do banks estimate non-maturity deposit behavior?

Banks estimate non-maturity deposit (NMD) behavior by analyzing historical account data to separate the stable “core” portion of deposits from the more volatile “non-core” portion. The core portion is assigned a behavioral maturity that extends well beyond the contractual overnight term, while the non-core portion is treated as short-term and rate-sensitive.

The estimation process typically involves two key dimensions. First, banks assess deposit stability by looking at how balances have moved over time across different rate environments. Deposits that have remained relatively constant through past rate cycles are classified as more stable and assigned longer behavioral maturities. Second, banks assess rate sensitivity, meaning how much of a rate increase the bank would need to pass on to depositors before they move their money elsewhere.

Basel.NXT and the EBA’s IRRBB guidelines set specific caps on the behavioral maturities banks can assign to NMDs. For retail deposits, the regulatory cap is typically five years for the average repricing maturity, and ten years for the longest maturity bucket. Wholesale deposits face tighter caps given their higher rate sensitivity. Banks working under frameworks such as APS 117 in Australia or the Basel 3.1 standards in Europe must stay within these regulatory bounds even if their internal models suggest longer stability.

Segmentation matters a great deal here. Retail current accounts behave differently from small business deposits, which behave differently again from high-net-worth savings accounts. The more granular the segmentation, the more accurate the behavioral model, and the better the bank understands where its real interest rate exposure sits.

What is prepayment modeling and why does it affect IRRBB?

Prepayment modeling is the process of estimating the rate at which borrowers will repay loans ahead of schedule. It affects IRRBB because early repayment of fixed-rate loans shortens the effective duration of a bank’s asset book, changing how the balance sheet responds to interest rate movements and creating reinvestment risk when rates have fallen.

When rates drop, borrowers have a strong incentive to refinance their fixed-rate mortgages or personal loans at lower rates. This accelerates prepayments precisely when reinvestment opportunities are least attractive for the bank. Conversely, when rates rise, prepayments slow down and the bank is locked into lower-yielding assets for longer. Both scenarios create asymmetric risk that needs to be captured in IRRBB calculations.

Banks typically model prepayment rates using a combination of approaches:

• Historical prepayment rates observed across different product types and rate environments
• Rate incentive models that link prepayment probability to the difference between the contract rate and current market rates
• Seasoning curves that reflect how prepayment behavior changes over the life of a loan
• Macroeconomic variables such as housing market activity, unemployment, and consumer confidence

Prepayment assumptions feed directly into both EVE and NII calculations. For EVE, they affect the present value of future cash flows. For NII, they affect the timing and volume of cash flows available for reinvestment in the near term. Getting these assumptions wrong in either direction can materially misstate a bank’s interest rate risk position.

How do banks validate and update their behavioral assumptions over time?

Banks validate behavioral assumptions by back-testing model predictions against actual observed behavior, then recalibrating the models when the predictions diverge materially from reality. This is an ongoing process, not a one-time exercise, because customer behavior shifts with interest rate cycles, economic conditions, and changes in the competitive environment.

A robust validation process typically covers several areas. Back-testing compares predicted prepayment rates, deposit attrition, and repricing patterns against what actually happened in the portfolio over a defined lookback period. Sensitivity analysis tests how much the IRRBB output changes when key assumptions are varied, which helps identify which assumptions have the most material impact on risk measures. Benchmarking compares internal assumptions against industry norms and regulatory guidance to identify outliers that may need justification.

The frequency of updates depends on the volatility of the underlying behavior. In a stable rate environment, annual recalibration may be sufficient. After a significant rate move, such as the rapid tightening seen in 2022 and 2023, banks need to reassess their NMD and prepayment assumptions more urgently because the historical data supporting those assumptions may no longer be representative.

Model governance plays an important role here. Banks need clear documentation of how each behavioral model was built, what data was used, and when it was last reviewed. Regulators expect this documentation to be available during supervisory reviews, and internal audit teams use it to assess whether the models are fit for purpose.

What’s the difference between behavioral assumptions for EVE and NII?

Behavioral assumptions for EVE focus on the long-run, present-value impact of interest rate changes across the full remaining life of the balance sheet, while NII assumptions focus on the short-term cash flow impact, typically over a one-to-two year horizon. The same underlying behavior can have very different implications for each metric depending on the time horizon and discount rate used.

For EVE, behavioral assumptions determine how long each cash flow stays on the balance sheet and at what rate it will reprice. A long behavioral maturity assigned to core deposits reduces EVE sensitivity because those liabilities effectively hedge long-duration assets. A short behavioral maturity increases EVE sensitivity because the funding reprices quickly and the bank is exposed to higher funding costs if rates rise.

For NII, the focus is on what happens to interest income and expense in the near term. Here, the question is not just about maturity but about repricing timing and volume. A bank might have stable long-term deposits from an EVE perspective but still face NII pressure if a large proportion of those deposits reprice upward quickly when rates rise.

This distinction creates a tension that banks need to manage carefully. Strategies that reduce EVE sensitivity, such as extending asset duration or lengthening deposit behavioral maturities, can increase NII volatility in the short term, and vice versa. Regulators expect banks to monitor and manage both metrics simultaneously, which is why the IRRBB framework requires reporting under multiple interest rate scenarios across both dimensions.

How do regulators assess banks’ behavioral assumption models?

Regulators assess behavioral assumption models by reviewing whether they are well-documented, empirically grounded, and consistent with the regulatory caps set out in the IRRBB standards. Under Basel.NXT and frameworks such as APS 117, supervisors expect banks to demonstrate that their assumptions are based on observed customer behavior, not just management judgment, and that the models are subject to regular independent validation.

The key regulatory reference points include the Basel Committee’s 2016 IRRBB standards, the EBA’s guidelines for EU banks, APRA’s APS 117 for Australian institutions, and equivalent national implementations of Basel 3.1. Each of these frameworks sets out minimum requirements for NMD segmentation, caps on behavioral maturities, prepayment assumption documentation, and stress scenario design.

During supervisory reviews, regulators typically look at:

• Whether the bank’s behavioral maturities for NMDs stay within regulatory caps
• Whether prepayment assumptions are back-tested and updated regularly
• Whether the assumptions are applied consistently across EVE and NII calculations
• Whether stress scenarios include changes to behavioral assumptions, not just interest rate shocks
• Whether there is a clear audit trail from raw data through to the final IRRBB metrics

One area regulators pay particular attention to is whether banks are using behavioral assumptions to artificially reduce their reported IRRBB exposure. Assigning very long behavioral maturities to deposits or very low prepayment rates to fixed-rate loans can make a bank look less rate-sensitive than it really is. Supervisors compare assumptions across peer institutions and challenge outliers.

This is where having a platform that supports transparent, auditable behavioral modeling makes a real difference. Our IRRBB solution within the Basel.NXT suite includes dedicated behavioral modeling tools, a Curve Management UI, and a guided scenario analysis workspace that keeps every assumption traceable from input to output. That level of transparency is exactly what regulators want to see, and it makes supervisory reviews considerably less stressful for the teams involved.

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