Regulation outlier tests of IRRBB deposit models highlight how small changes in behavioural assumptions can generate materially different interest rate risk profiles and capital strains across banks. As supervisors digest the first wave of interest rate risk in the banking book results, sharp dispersion in metrics derived from supposedly comparable portfolios is raising questions about whether non-maturing deposit modelling truly reflects customer behaviour or is being stretched to avoid breaching thresholds.
This article examines how supervisory outlier tests for economic value and earnings are reshaping views on interest rate risk, why the emerging pattern of results is eroding confidence in peer models, and what this means for governance, validation, and regulatory engagement. Readers will gain a structured view of the evolving framework, the sources of divergence in model outcomes, and the practical steps needed to strengthen assumptions, documentation, and supervisory dialogue.
Regulatory Landscape
Core prudential standard: Interest rate risk in the banking book is anchored in the Basel Committee on Banking Supervision standard, which defines IRRBB as the current or prospective risk to capital and earnings from adverse interest rate movements and requires supervisors to apply outlier tests at minimum against the change in economic value relative to Tier 1 capital under prescribed shocks, with a benchmark threshold of 15% for identifying outlier banks.
Pillar 2 implementation: Basel’s enhanced Pillar 2 approach mandates that banks maintain robust frameworks for identifying, measuring, monitoring, and controlling interest rate risk, with governing bodies responsible for risk appetite, policy limits, and oversight of behavioural and modelling assumptions that must be conceptually sound, documented, and rigorously tested.
European framework: In the European Union, the European Banking Authority has translated the Basel principles into detailed guidelines on IRRBB and credit spread risk and regulatory technical standards on supervisory outlier tests, with the EBA’s package on IRRBB, CSRBB, and reporting now fully operational and integrated into prudential processes such as SREP; the relevant texts and guidance are accessible via the EBA’s official website at European Banking Authority.
Supervisory outlier tests: The EBA’s standards require banks to calculate economic value of equity and net interest income sensitivities under a common set of interest rate scenarios, including a constant balance sheet assumption for the earnings test, and apply quantitative thresholds so that supervisors can detect institutions whose risk profile or behavioural assumptions appear excessive or insufficiently conservative.
Supervisory review process: In the euro area, the Single Supervisory Mechanism uses supervisory outlier test metrics as the anchor for the automatic score of IRRBB risk level within the supervisory review and evaluation process; joint supervisory teams then refine this with a qualitative assessment of banks’ control frameworks, governance, and adherence to level 1 regulations, as outlined by the European Central Bank’s SREP methodology, which is publicly available on the ECB banking supervision site at ECB Banking Supervision.
Why This Happened
Sharp rate cycle and balance sheet strain: The rapid transition from a prolonged low-rate environment to an aggressive tightening cycle exposed structural mismatches in the banking book and amplified the sensitivity of deposit models, drawing heightened attention to how non-maturing liabilities and embedded optionality are captured in IRRBB measurement.
Historical underweighting of IRRBB: For years, interest rate risk in the banking book was often treated as a secondary concern relative to credit and market risks, with many institutions using overly simplified deposit modelling and limited behavioural segmentation, leaving frameworks misaligned with the more demanding expectations articulated by Basel and regional regulators.
Regulatory focus after high-profile failures: Events such as the failure of Silicon Valley Bank demonstrated how inadequate interest rate risk management, concentrated fixed-rate asset exposures, and unstable deposit bases can rapidly translate into solvency and liquidity stress, reinforcing regulators’ determination to tighten expectations and close gaps in banks not subject to full-scope Basel regimes.
Standardisation via supervisory outlier tests: By imposing common shock scenarios and requiring constant balance sheet assumptions in some tests, regulators intended to improve comparability across institutions, but this has instead exposed the degree to which differences in behavioural assumptions and modelling choices for deposits drive diverging metrics, fuelling scepticism about how realistic some parameters are.
Data and modelling weaknesses: The first rounds of thematic reviews and inspections have revealed recurring issues, including incomplete exposure capture, weak reconciliation between finance and risk data, sparse documentation of assumptions, and outdated internal metrics, all of which undermine confidence in reported interest rate risk positions and intensify supervisory pressure.
Impact on Businesses and Individuals
Capital and earnings volatility: Divergent model results mean that two banks with similar balance sheets can report very different impacts on economic value and net interest income under supervisory scenarios, potentially altering Pillar 2 capital requirements, internal risk limits, and investors’ perception of resilience.
Governance and accountability: The Basel framework and regional implementations stress that boards and asset–liability committees are accountable for understanding and challenging key behavioural assumptions, including deposit repricing and stability, and must integrate interest rate risk into business planning, budgeting, and risk appetite, raising the stakes for senior management oversight.
Operational and model risk: Weaknesses in data integrity, behavioural segmentation, and documentation create not only regulatory risk but also model risk, as inaccurate or over-optimistic assumptions about deposit behaviour can prompt misguided hedging, pricing, and product decisions that affect profitability and competitive positioning.
Compliance workload and cost: Meeting new reporting and testing requirements for supervisory outlier tests demands investment in systems capable of rate-sensitive cashflow projection, granular segmentation of non-maturing deposits, and enhanced validation processes, raising operational costs but also enabling more sophisticated balance sheet steering for those that adapt effectively.
Consequences for customers and markets: As banks recalibrate their models and risk appetite frameworks, deposit pricing, product design, and the stability of funding strategies may change, with potential implications for depositors, borrowers, and broader market dynamics, especially where institutions adjust term structures or shrink balance sheet exposures in response to heightened supervisory scrutiny.
- Legal and regulatory exposure: Institutions that fail supervisory tests or cannot justify their modelling choices may face higher capital add-ons, binding remediation plans, restrictions on dividend distributions, or, in severe cases, enforcement actions under national prudential laws.
- Individual liability: Senior managers responsible for risk, finance, and treasury may see increased personal accountability where governance failures in interest rate risk management are identified, particularly in jurisdictions that apply fit and proper or accountability regimes.
Enforcement Direction, Industry Signals, and Market Response
Intensified supervisory challenge: Supervisors are increasingly scrutinizing the credibility of deposit models, especially where banks report favorable supervisory outlier test metrics that appear inconsistent with peer experience or market benchmarks, leading to targeted requests for sensitivity analyses, back-testing evidence, and alternative scenario runs.
Use of qualitative judgements: While quantitative thresholds for economic value and earnings metrics remain central, supervisory teams are placing greater emphasis on qualitative assessments of governance, behavioural modelling, and validation, adjusting risk scores and capital expectations where control frameworks are judged weak even if outlier test thresholds are not breached.
Emerging themes from thematic reviews: Recent supervisory exercises and consultancy reviews highlight recurring gaps such as underdeveloped reverse stress testing for interest rate risk, inadequate segmentation of non-maturing deposits, and poor integration of credit spread risk, signalling where regulators are likely to focus on follow-up work and remedial expectations.
Industry model recalibration: Many institutions are revisiting their interest rate risk frameworks by enhancing behavioural models, incorporating richer datasets, and improving alignment between internal risk metrics and regulatory measures, with some banks using the supervisory constant balance sheet construct as a baseline while building more dynamic internal simulations for management purposes.
Market transparency and peer comparison: Analysts and investors are increasingly attuned to disclosures on interest rate risk sensitivity, deposit stability, and supervisory feedback, which means that large divergences from peers can trigger questions about model credibility and risk appetite, reinforcing the need for consistent narratives between public reporting and regulatory submissions.
Compliance Expectations and Practical Requirements
Clarifying risk appetite and limits: Institutions should articulate risk appetite for both economic value and earnings-based measures, establishing limits that are consistent with supervisory outlier test metrics and clearly linked to governance processes, escalation protocols, and strategic decision-making under different rate scenarios.
Strengthening behavioural modelling: Banks need to reassess the foundations of their deposit models, including segmentation by customer type and product, repricing behaviour, and stability assumptions, supporting parameters with empirical evidence and ensuring consistency between management models and those used for regulatory reporting.
Enhancing validation and documentation: A robust model risk management framework should cover independent validation of interest rate risk models, systematic back-testing of deposit behaviour, sensitivity analyses for key assumptions, and comprehensive documentation that explains methodologies, data sources, and governance, ready for supervisory review and challenge.
Improving data quality and reporting: Achieving reliable supervisory outlier test results requires reconciled, complete datasets across finance and risk, rate-sensitive cashflow projections, and the ability to generate required metrics and templates at the required reporting frequency, aligned with the implementing standards for IRRBB reporting.
Integrating IRRBB into enterprise planning: To move beyond a narrow compliance posture, institutions should embed interest rate risk analysis into budgeting, capital planning, product pricing, and strategic ALM decisions, using the same core datasets and behavioural assumptions so that management, regulators, and markets see a coherent picture of risk and performance.
Common pitfalls to avoid: Overreliance on expert judgement without empirical support, inconsistent treatment of deposits across internal and regulatory models, failure to perform reverse stress testing to identify breaching conditions, and weak board engagement with methodological choices can all erode supervisory confidence and increase capital or remedial burdens.
Practical steps for remediation: Banks can prioritise targeted reviews of non-maturing deposit models, invest in enhanced ALM and reporting infrastructure, organise joint workshops between risk, treasury, finance, and internal audit, and establish structured supervisory engagement plans to explain model changes and demonstrate control enhancements.
As supervisory outlier tests mature and more data accumulates across institutions and rate cycles, regulators are likely to sharpen their expectations on deposit modelling realism, challenge aggressive assumptions more forcefully, and refine benchmarks for acceptable dispersion in risk metrics; banks that invest early in high-quality behavioural models, data integrity, and transparent governance will be better positioned to navigate future scrutiny, support strategic balance sheet management, and maintain trust with supervisors and markets as standards evolve.
FAQ
1. How do supervisory outlier tests affect banks with aggressive deposit modelling?
Ans: Institutions that rely on optimistic assumptions about deposit stability or repricing may initially report favourable economic value or earnings metrics, but supervisors can challenge these models during reviews, require sensitivity analyses under more conservative parameters, and, if necessary, impose higher capital requirements or remedial actions when results appear misaligned with peer data or observed behaviour.
2. Why are non-maturing deposits such a focal point in IRRBB supervision?
Ans: Non-maturing deposits embody significant behavioural uncertainty because customers can change balances and pricing expectations in response to market conditions; small changes in assumed average maturity, repricing speed, or pass-through rates can materially alter interest rate risk metrics, so supervisors view these assumptions as a key driver of model dispersion and potential underestimation of risk.
3. What governance practices do regulators expect for interest rate risk in the banking book?
Ans: Regulators expect boards and senior management to define risk appetite for economic value and earnings measures, approve policies and limits, understand and challenge key behavioural assumptions, ensure independent validation of models, and integrate interest rate risk considerations into business planning, budgeting, and capital management, supported by clear documentation and regular reporting.
4. How can banks improve the credibility of their IRRBB models with supervisors?
Ans: Banks can enhance credibility by grounding behavioural assumptions in robust empirical data, performing back-testing and sensitivity analyses, maintaining consistent methodologies across internal and regulatory metrics, addressing data quality issues, documenting models thoroughly, and engaging proactively with supervisors to explain modelling choices, model changes, and the governance processes surrounding them.
5. What role does data quality play in successful IRRBB compliance?
Ans: High-quality, reconciled data across finance and risk functions is essential for accurate cashflow projections, scenario analyses, and reporting under supervisory outlier tests; poor data integrity can undermine metrics, erode supervisory trust, and complicate compliance, whereas robust data infrastructure supports both regulatory submissions and internal decision-making on hedging, pricing, and capital planning.