ALM and IRRBB: lessons from the first round of reviews

Interest rate risk in the banking book (IRRBB) and credit spread risk in the banking book (CSRBB) are under renewed scrutiny as European supervisors increase their focus on balance sheet resilience and risk governance. While regulatory expectations are well defined through the EBA Guidelines, BCBS standards, and RTS on Supervisory Outlier Tests, recent thematic reviews and on-site inspections have revealed that many institutions are still falling short.

Grant Thornton works alongside banks to address these gaps, supporting the design, implementation, and validation of effective IRRBB frameworks that stand up to both scrutiny and market volatility.

Common challenges across the industry

Strategy and governance gaps

Many institutions lack a clear, actionable IRRBB strategy. Risk appetite frameworks and internal limits are often misaligned with the balance sheet profile. Weak governance structures and unclear ownership further limit integration of IRRBB into financial and treasury planning, particularly in relation to deposit evolution, repricing assumptions, and hedging decisions.

Behavioural modelling weaknesses

Behavioural modelling remains one of the weakest links. Banks frequently rely on oversimplified assumptions for non-maturing deposits (NMDs), prepayment rates, and equity treatment, often without sufficient statistical justification. This leads to inaccurate Economic Value of Equity (EVE) and Net Interest Income (NII) projections, and an inability to meet the standards set out for Supervisory Outlier Tests.

Reverse stress testing is often missing or underdeveloped, limiting the bank’s ability to assess where IRRBB thresholds might be breached under adverse conditions. The EBA highlights the need for enhanced NMD segmentation (PDF), stress testing including reverse stress tests, and supervisory expectations on behavioural assumptions based on observable risk factors.

CSRBB framework limitations

The CSRBB framework is equally underdeveloped. Institutions struggle to define, measure, and monitor spread risk consistently, often omitting material exposures from internal and regulatory metrics. This results in incomplete coverage of structural risks and weak integration with ICAAP and risk appetite statements.

Data and modelling under pressure

Measurement precision and documentation gaps

Data integrity and measurement precision are recurring concerns. Institutions frequently use datasets that lack reconciliation between finance and risk functions, with incomplete capture of both on- and off-balance sheet exposures. Documentation is sparse, modelling assumptions are not well justified, and internal metrics are often outdated or inconsistent with regulatory expectations. Cashflow modelling is not always rate-sensitive or scenario-dependent, which further undermines the quality of EVE/NII simulations.

Concerns with data integrity and measurement precision include:

• Lack of reconciliation
• Incomplete exposures
• Sparse documentation
• Outdated internal metrics

Model validation and oversight

Banks model monitoring and validation frameworks are under pressure. Tests on model soundness, parameter sensitivity, data representativeness, and outcome stability are often absent or undocumented. Managerial overlays are applied without adequate independent challenge, weakening the overall control environment and increasing model risk.

NMD modelling issues

Banks frequently overstate the stability and duration of deposit volumes. Segmentation techniques lack granularity, behavioural clusters are not statistically validated, and outdated assumptions are not revisited in light of changing customer behaviour. This undermines both liquidity and interest rate risk assessments. According to supervisory guidance, institutions should apply internal modelling assumptions (PDF) for commercial margins in the SOT on NII where available; otherwise, a constant spread over the risk-free rate should be used.

Turning compliance into strategic advantage

To complement regulatory thresholds, additional metrics such as changes in fair value of instruments, embedded gains/losses, and cost structure sensitivity can help determine whether IRRBB exposures are adequately mitigated even if thresholds are breached.

Grant Thornton helps banks navigate these challenges by offering targeted support across the IRRBB and ALM lifecycle. We work with institutions to design robust frameworks aligned with regulatory expectations, implement data-driven behavioural models, and build rate-sensitive cashflow projection tools. Our experts bring deep experience in model validation, ensuring that assumptions, methodologies, and outputs stand up to scrutiny and support sound decision-making.

We also assist in strengthening governance: formalising risk ownership, documenting methodologies, establishing model tiering, and enhancing reconciliation between front office, risk, and finance data. Through our ALM advisory services, we help link IRRBB with business strategy, refining hedging, planning, and scenario design in line with market realities.

As regulatory focus intensifies, IRRBB is no longer just a compliance issue, it is a strategic imperative. Grant Thornton partners with financial institutions to transform IRRBB into a strength, delivering resilience, insight, and sustainable value.