IRRBB: 5 Top Challenges for large European banks
05 July 2018
While managing interest rate risk in the banking book (IRRBB) is not new to banks, the rigour required by the recent Basel standards (BCBS368) has presented challenges to the industry. Though the standards are yet to be written into European regulation, most major banks are working toward compliance by the year-end target sought by Basel. These tight timelines, and the associated regulatory scrutiny, have led to large change programs and a significant investment of time and resource.
While each institution faces its own particular challenges in complying with BCBS 368, we have found five common themes which come up time and again across the industry:
- Behavioural modelling: Even though behavioural modelling is well established in Treasury, it remains a challenge for several reasons. Firstly, in contrast to the trading book, there is no industry standard modelling framework for the banking book. Secondly, there is no common source of public data; each bank has to rely on its internal data sets. Thirdly, the long period of low and stable interest rates means recent historical data may have limited relevance to the IRRBB shock scenarios. Finally, these data issues lead to the extensive use of expert judgement.
- Net Interest Income (NII) forecasting: Development of NII models is (naturally) a particular challenge for those institutions that have historically steered the balance sheet based on value metrics alone. Even for banks accustomed to NII modelling, the challenge of BCBS368 is to combine realistic business forecasting with rigorous quantitative methods backed by data. In particular, as banks advance from static to dynamic balance sheet forecasts, the goal is alignment of NII forecasts with the internal business plan while maintaining the required rigor.
- Model governance: In most institutions IRRBB models, and Treasury models more broadly, are a new area for model risk management functions. With the typical Treasury model validation team being less than three years old, many models are being validated for the first time by individuals who have a quantitative rather than a Treasury background. This model type poses specific challenges in terms of data issues, the extensive use of expert judgement assumptions and the intertwining of modelling and business decisions.
- The evolving regulatory framework: At the time of writing, the Basel standards (BCBS 368) are yet to be “written into law” by European regulators, even though the standards require compliance within 2018. Banks face the challenge of complying with a moving target, while continuing to adhere to existing regulation.
- Data and systems: The data used in IRRBB modelling and MI is typically accessed through multiple layers of legacy systems, using a variety of data architectures and a multiplicity of manual patches. Long & costly change cycles create an inflexibility that is at odds with the need to respond promptly to ad-hoc regulatory (or management) requests.
Though each institution’s response to these challenges varies, it is common to see the introduction of Treasury specific model validation, investment in IT and data infrastructure, the on-boarding of quants to support ALM and the potential for the enhanced rigour in balance sheet forecasting (NII) to be used in the strategic planning process. If managed well, compliance with BCBS 368 should lead to a stronger ALM function, with more rigorous risk management capabilities and an enhanced relationship with planning and strategy.