Managing a bank's balance sheet implies addressing multiple structural risks, with Interest Rate Risk in the Banking Book (IRRBB) being one of the most significant due to its direct impact on profitability and solvency. In this article, we will discuss the two main approaches to measuring this risk, followed by an exploration of how to project Net Interest Income (NII) and the main challenges when forecasting it, with a focus on the most widely used practices in the industry.
Audio Article
The Basel regulatory framework defines two complementary approaches for measuring IRRBB. One is focused on the income statement, and the other on the economic value of the balance sheet. Both assess the entity’s sensitivity to interest rate movements but with different time horizons and objectives.
We will briefly explain both approaches to measuring IRRBB, focusing on the Net Interest Income approach.
Net Interest Income (NII) is the difference between the interest income from the institution’s assets and the interest expense from its liabilities over a specified time horizon.
In an environment of volatile interest rates and increasing regulatory pressure, accurate estimation of NII has become a strategic tool for ALM teams.
What is needed to estimate a financial institution’s NII correctly?
As discussed, financial margin involves projecting interest income and expense over a specific period. Accurate estimation depends on a range of assumptions and parameters; different assumptions will yield different figures and interpretations.
For this article, we´ll simplify NII forecasting assumptions into three major components:
One of the most critical elements in estimating a company’s NII is defining the time horizon. Whether the projection is for 6 months, 1 year, 2 years, or 3 years, assumptions must be made about new business during that period. NII is derived from projecting future asset and liability volumes over the specified horizon.
Although the 12-month NII projection remains the main regulatory and internal focus, many institutions are aligning with Basel’s recommendations by adopting multi-horizon and overlapping projections. These allow analysis of interest rate impacts over various timeframes (6 months, 1 year, 2 years, etc.), improving the ability to anticipate inflection points in financial margin and supporting stronger strategic decision-making.
Behavioral models are essential for accurately forecasting the future behavior of certain financial products with no clear contractual maturity or that permit early termination. Key examples include demand deposits, savings accounts, and loans with prepayment options. These models simulate customer reactions to changes in interest rates, market conditions, or commercial incentives, something critical for correctly projecting expected cash flows.
For instance, non-maturity deposits use models to estimate effective average life and sensitivity to offered remuneration. Mortgage loans, in turn, rely on prepayment models influenced by rate changes, refinancing campaigns, or regulatory shifts. Proper calibration of these models is vital to avoid significant deviations in margin estimates, especially under stress or high volatility scenarios.
While institutions know what’s currently on their balance sheet, they cannot precisely predict what new business will be generated in the future. As time progresses and transactions are completed, new operations arise: this is referred to as “New Business”, and it plays a key role in NII estimation.
For example, if a bank projects over a two-year horizon, it must estimate how its balance sheet will evolve in terms of volume, products, and conditions over that period. The central question becomes: how much new business will be generated, and under what terms?
This is arguably one of the most critical components since the projected NII will vary significantly based on the volume and type of projected business. There are two main approaches for estimating new business:
The current balance sheet size is maintained, and only maturing or amortized positions are replenished. This approach avoids altering the institution’s current situation and is commonly used in regulatory reporting required by supervisors.
The balance sheet is adjusted based on projected growth or decline in asset and liability items. Institutions typically establish business growth budgets for retail segments (based on historical trends and market insights) and also plan wholesale funding and asset purchase strategies (terms, amounts, etc.).
With recent advances in computing and data analytics, some institutions are starting to adopt machine learning-based predictive models to estimate new business. These models incorporate macroeconomic variables, customer behavior patterns, and commercial campaigns, allowing more granular and adaptive balance projections. Especially in volatile or evolving regulatory environments, this approach enhances forecast accuracy. Furthermore, it’s essential to align new business projections with the institution’s risk appetite and strategic goals, an area that is receiving increasing attention from regulators.
The evolution of balance sheet volumes, along with the application of sophisticated models, determines how much new business must be originated to offset maturing positions and forecasted operations. It also affects the final interest rate of assets, liabilities, and off-balance sheet items. These estimates directly impact NII simulations.
Ultimately, accurately estimating Net Interest Income is not just about forecasting financial performance. It becomes a critical tool for evaluating the resilience of the business model under adverse scenarios. Margin sensitivity, combined with a strategic view of the balance sheet, enables institutions to protect their capacity for organic capital generation, an aspect increasingly valued by both regulators and investors.
In upcoming articles, we will discuss adverse scenarios and margin sensitivity – the ultimate goal of NII management in ALM. This requires estimating interest rate shocks and their implications on new business and behavioral models, and therefore, on margin sensitivity.
Enjoyed this article? Subscribe to our blog for expert insights on ALM, structural risks, and strategic banking practices — delivered straight to your inbox.
👉 Join the Mirai RiskTech community: