The continued expansion of regulation around Interest Rate Risk in the Banking Book (IRRBB) presents a series of challenges to CFOs and Heads of Risk at banks and building societies. These challenges – including data provenance and transparency, data quality and availability, operational capacity, and availability of expertise – often stem from limitations in the technology available to those institutions.

Developments in IRRBB analysis have historically revolved around the range of interest rate stress scenarios that are modelled and in adjustments to the current balance sheet when preparing the repricing mismatch data that underpin any IRRBB analysis.

More recently, the focus in IRRBB analysis seems to be pivoting to the determination of interest rate risk as part of financial planning.

This article identifies some of the key benefits of this approach and explores how technology, including our own Whistlebrook Asset Liability Management (WALM) system, can overcome the challenges faced and provide a more effective interest rate risk management strategy.

Will modelling IRRBB on data that is itself modelled really deliver benefits?

It is reasonable to ask whether a financial plan is appropriate as a data source for IRRBB analysis – the future balance sheets are dependent on the assumptions in the plan and these assumptions will all contribute a degree of uncertainty into the data that is generated.

One may also question whether the stress scenarios currently in operation remain relevant when used to stress the future balance sheet.

Firstly, the financial plan must be realistic and achievable to maintain shareholder confidence. Moreover, if the plan’s assumptions prove true, the future balance sheet will become the current balance sheet. The assumptions within the plan are merely a description of that journey and do not therefore directly impact the IRRBB analysis.

Secondly, when running IRRBB analysis on the current balance sheet, it is customary to model a fixed set of stress scenarios from one period to the next for extended periods. As such, questions over the relevance of these scenarios in a future model are moot.

Technology can provide the means to address these concerns by modelling a variety of financial plans and widening the range of stress scenarios that are applied in the IRRBB analysis.

What are the benefits of modelling IRRBB on future balance sheets?

Time is a great asset to have in a business and it is very often in short supply. The earlier potential risks can be identified, the greater the opportunity to avoid or effectively mitigate those risks.

The consequence of a loss in economic value or future earnings can be existential for an institution. When running IRRBB on the current balance sheet, the nature of the interval range that is used could cloak any potential risk which then materialises in the short to medium-term intervals. As management actions in this situation can be extremely reactive, their success is unlikely to have been modelled, and this fact introduces uncertainty which can lead to loss of confidence.

Armed with a system like WALM, analysts can model interest rate risk on multiple scenarios, develop and test mitigation strategies, and run sensitivity analysis on those assumptions for which there is less certainty. This comprehensive approach, when combined with future measures of key ratios such as LCR and the capital ratios, goes a long way in enabling institutions to answer the following question. Will the institution continue to conform to its risk appetite statement and regulatory requirements within a given financial plan?

Identifying and resolving the challenges

The depth of analysis required to model IRRBB on future balance sheets is significant and it is inevitable that a systemic approach is the only means to meet this challenge.

Availability of data is key, as is its provenance. A system like WALM will integrate with an organisation’s central database to retrieve the data used for regulatory reporting, ensuring confidence and consistency. This data will be used both for reporting on the current balance sheet, and for seeding the financial plans.

In times of volatility following a period of market tranquillity, data bias also becomes a more significant issue. Behavioural curves and assumptions inferred from historical data may not reflect the reality of today and the future. There may not even be a suitable dataset from which to derive expectations. Having a system which supports multiple financial plans with a comprehensive range of assumptions allows users to explore each assumption in depth to understand to what degree a level of uncertainty will impact the overall IRRBB analysis results. In this way, the conclusions drawn from those results can be qualified.

IRRBB analysis is often conducted by a few specialised individuals in an institution or in some cases, by a single person. As such, the knowledge and experience held in the institution is subject to key person risk. This is especially the case when IRRBB is modelled using spreadsheets.

The systemic approach mitigates this risk through a consistent, verifiable approach supported by the system documentation and third line support. WALM, as an example of such a system, promotes transparency and auditability in both the financial plan generation and in the IRRBB analysis. And as WALM does, the system should also generate consistent and clear reports, dashboards, and charts for ready inclusion in the ALCO pack.

IRRBB analysis will endure as a valuable risk management tool, even as interest rates stabilise.

It is unsurprising in these times of interest rate volatility alongside a backdrop of economic uncertainty that IRRBB is very much in the foreground in risk management.

But what of a period of stagnation and long-term stability? It could be suggested that this is exactly the time when IRRBB analysis of future balance sheets should be a key focus. Economic conditions will always be variable over time to a greater or lesser extent. This is where the longer view of IRRBB is invaluable in ensuring that the financial plan considers both the strategic growth of the institution and building in the resilience to ride future shocks.

And this is where the technology can really prove its worth: Enabling comprehensive modelling and ‘what if’ analysis; establishing risk metrics and, through them, evaluating the effectiveness of management actions and variations to the asset-liability mix; assessing whether the inherent interest rate risk is consistent with the institution’s view on risk-return. Furthermore, the evidence is stored and demonstrable when audit comes to call.

If you would like to watch Whistlebrook’s on-demand IRRBB webinar:

Whistlebrook customers can access it on the Resource Centre.

Everyone else can watch it here. You will need to fill out a few details and you will be able to view the full webinar at your convenience.