‍This article will cover the new challenges introduced by the most recent regulatory requirements for Interest Rate Risks for Banking Book (IRRBB) and Credit Spread Risk in the Banking Book (CSRRBB).  Applicable to all banks in the EU, it should be of strong interest to their Asset and Liability Management teams being in charge of the IRRBB calculation.

ALM Teams pressured by System Limitations to Navigate Shock Results and Anticipate New IRRBB Challenges

In the current economic landscape characterised by rising interest rates and uncertainty surrounding inflation, that may lead to significant deposit outflows if combined with a non competitive remuneration strategy or a liquidity mismatch. For those reasons, interest risk management has become the focal point of intense regulatory activity, drawing closer scrutiny from financial institutions.

On the one hand, the European Banking Authority (EBA) published final versions of the guidelines (10/2022) and of its technical standards (07/2023) with regard to Interest Rate Risk in the Banking Book (IRRBB) and Credit Spread Risk in the Banking Book (CSRBB), trying to ensure harmonisation, to reflect proportionality and to enhance legal certainty to manage and mitigate on an appropriate scale the interest rates and credit spread risks across institutions. Starting from 2023, June 30th (exception of sections related to CSRBB planned on 2023, December 31st) the European banks will have a one-year period to comply. The new methodology and constraints will impact multiple aspects of the current chain, from the modelling to the IT architecture, including the challenge around the availability and quality of the data.

On the other hand the European Central Bank (ECB) is conducting an on-site inspection campaign dedicated to Interest Rate Risk in the Banking Book (IRRBB), focusing on both modelling techniques and the global IRRBB framework, including the IT infrastructure.

This inspection is highlighting significant structural data quality issues, a constant challenge to reconcile accounting-management data and a limited degree of readiness to timely execute, ad hoc demands on the Supervisory Outlier Test (SOT) with specific assumptions (e.g. modifying the maturity term of a specific product) and, more globally, upcoming evolutions.

Additionally of the challenge of new requirements, the economic situation and unclear forecasts of client’s behaviour lead to the need for a more accurate scrutiny of the bank risk exposure to streamline hedging strategy or enhance a competitive advantage.

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With High Stakes: Driving Granularity, Data Quality, and New Metrics…

Among the various evolutions, new criteria are being introduced, each presenting its own difficulties and impacts on processes and IT systems:

• To identify and assess the IRRBB with prudent behavioural assumptions impacting the repricing of non-maturity products (e.g. a five-year cap floor for retail and non-financial wholesale deposits). The main challenge here is determining how to process "proxy" data to limit granularity due to the portfolio volume before running models. This could result in a moderate to high IT impact.
• To extend the Supervisory Outliers test on NII (Net Interest Income) and not only to EVE (Economic Value of Equity), with constant balance sheet assumptions. This involves replacing maturing products with new cash flows having comparable features. The primary challenge is managing mixed assumptions on forecasted liabilities. It could lead to a low to moderate IT impact.
• To  explicitly and comprehensively calculate and monitor the CSRBB using an ad hoc internal system, controlling both EVE and NII and incorporating market value changes. The assessment of the CSRBB will encompass both assets and liabilities, and will be evaluated on a net basis. In practice, this will require decomposing credit spread rates into theoretical components, the reallocation of which may be a challenge within the aggregation systems. Additionally it will require thousands of new data points to be managed and more frequently refreshed (at least quarterly), resulting in structural and costly IT changes. The main challenge here is how to handle a variable scope and multi-sourced information with a very high volume of data (e.g., instruments traded in deep and liquid markets with observable market prices, details of securities portfolio, heterogeneous valuation, etc.). Consequently, this could lead to a high to very high IT impact.

The methodologies of calculation are undergoing evolution, and now they incorporate behavioural assumptions specific to each business line. These assumptions are to be applied at various aggregated levels of the portfolio and, separately, for each currency.

Additionally, as mandated by the EBA, the analysis of both IRRBB and CSRBB will generally require the consideration of multiple dimensions. These dimensions include not only currency but also other relevant factors from which risks may arise, such as sector or geographical locations.

Given these requirements, the Asset Liabilities teams seek a flexible and high-performing solution that can efficiently handle significant amounts of data without compromising the selection of dimensions or granularity. Such a solution should enable them to adjust macro or detailed assumptions and simulate multiple metrics on the fly before embarking on the complete modelling process.

While leading banks' IT teams are already diligently working on implementing enhanced solutions to meet these needs, mid-size banks may face challenges in fully addressing the operational complexities without substantial investments and with limited short-term return on investment.

Advanced Methodologies for Regulatory Compliance and Competitive Edge. Unlocking Opportunities?

The evolution of the regulation aims to  enhance the accurate management of interest and credit spread risk, presenting an opportunity to improve complex and discontinuous production chains. Modernising existing processes is not only necessary but also a prerequisite for supporting rapid forecasting and conducting precise bottom-up/top-down stress exercises that enable swift validation of effective management actions.

Let’s examine a few examples:

• Bottom-up: to assess and communicate the principal drivers of NII / EVE variations in response to divergent interest rate strategies between Asian and European central banks, the ability to drill down to impacted positions and risk profiles both globally and individually is crucial. Banks that consolidate all granular transactions in one place will gain a competitive edge, allowing them to focus on currency, entity, and location levels to fine-tune their local business strategies. In contrast, banks with traditional setups may struggle to drill down into consolidated reports, as local systems often support cash flow modelling with detailed information before feeding it into one aggregated system for conducting group stress tests, losing the granularity of information.
• Top-down: to assess the impact of a global rebalancing of structural FX hedges, it is essential to recalculate on the fly the underlying currencies and to compare the variations of metrics with the original scenario in a timely manner.

While significant progress has been made in developing guidelines for CSRBB, and to prevent any competitive disadvantage for European banks, EBA acknowledged that a room for interpretation remains and, thus, does not impose a standardised methodology. Therefore, banks are encouraged to consider their appetite for fine tuning this risk and should first focus on modernising their processes to support comprehensive analysis and to adapt to moving regulatory requirements.

How to Exceed Current Expectations and Anticipate Future Changes through Explainability and Simulation?

Using Opensee solution would bring the flexibility to explore and access the data with no more trade-off between volume, granularity and performance. Through Opensee’s technology, the ALM team would unlock the capacity to accurately and quickly “explain” the calculated results of interest risk across their organisation. Opensee’s collaborative platform not only provides full access to the whole dataset and dynamic reporting features to multiple users, it will enable the understanding of what is relevant, leveraging on native custom data quality checks and Python calculator capacities that could be easily updated.

In conclusion, with the evolving methodologies and growing complexity of risk analysis, it becomes imperative for banks of all sizes to invest in adaptable and robust IT solutions, similar to Opensee, to ensure accurate interest risk management and regulatory compliance while maintaining competitiveness in the market.Discover more about our Liquidity and ALM Risk solutions on our dedicated web page. Still curious about the topic? Read our blog “The Good, the Bad, and the Illiquid”.

‍About the author: Guillaume Felix joined Opensee as Head of Liquidity and Banking Book risks Solutions, leveraging 15 years of experience garnered in distinguished financial services consulting firms like EY advisory and Equinox-Cognizant. Throughout his career, he has honed expertise in credit risk, prudential regulation and data management for the Banking industry.