Interest rate volatility is an ongoing challenge for banks of all sizes. This is because rate changes can affect multiple aspects of a bank’s finances, such as its net interest income, funding costs, and liquidity. Understanding how to manage interest rate risk in banks requires forecasting methods that accurately reflect current market conditions. To achieve this goal, many banks use dynamic forecasting.

Dynamic forecasting helps banks anticipate and evaluate potential market changes before they occur. Unlike traditional forecasting, dynamic forecasting does not rely on static assumptions that can quickly become outdated, helping reduce potential blind spots. Banks are therefore able to better manage volatile environments through improved scenario modeling, decision-making, and risk management.

What Is Interest Rate Risk in Banking?

Interest rate risk is the possibility that changes in market interest rates can negatively impact a bank’s finances. Commonly affected areas include a bank’s capital, asset valuations, liquidity, and earnings.

Rate changes often result in the repricing of an institution’s assets and liabilities, each of which may be repriced on a separate timeline. This mismatch in timing is often what negatively impacts earnings.

For example, a bank may earn income from a portfolio of fixed-rate loans, but also have liabilities in the form of interest being paid out to deposit holders. If rates change, the bank may have to increase the interest paid to deposit holders to avoid losing deposits to competing institutions and creating liquidity challenges.

Earnings from the fixed-rate loan portfolio, however, would remain unchanged.

Understanding these relationships is fundamental to effective asset and liability management (ALM).

Why Traditional Interest Rate Risk Management Falls Short

Traditional interest rate risk management often involves the use of static assumptions, manual spreadsheet-based processes, and infrequent reporting cycles. While they may be sufficient for compliance and regulatory purposes, they fall short in volatile markets. As risk management trends evolve, forecasting accuracy has become increasingly important for effective decision-making.

As mentioned in Empyrean’s Dynamic Balance Sheet Forecasting webinar, static assumptions lead to inadequate and incomplete results. This is because they do not account for shifts in interest rates, customer deposit behavior, or changing market conditions. Simply put, reports may already be outdated by the time they are reviewed by decision-makers.

The ability to run multiple scenarios is also more difficult and error-prone due to the manual processes involved. Teams also tend to work in silos, leading to discrepancies in assumptions and data being used. Even when done correctly, traditional methods make it difficult to quickly evaluate multiple market conditions.

How Banks Manage Interest Rate Risk Today

To manage interest rate risk, banks today use a combination of forecasting, ALM modeling, and balance sheet monitoring. All of these are key in determining how interest rate changes may impact financial performance:

• ALM Modeling: Determines how assets, liabilities, and cash flows may shift in varying market conditions.
• Gap Analysis: Supplements other methods to identify mismatches in asset and liability repricing timelines.
• Earnings-at-Risk Analysis: Evaluates how net interest income and other areas of the balance sheet may be affected by interest rate shifts.
• Scenario Forecasting: Models outcomes based on different assumptions.
• Deposit Behavior Modeling: Focuses on how customer behavior is expected to change.
• Liquidity Monitoring: Evaluates funding stability, so banks can find alternative funding sources when needed.

Why Dynamic Forecasting Improves Interest Rate Risk Management

Dynamic forecasting provides more accurate and realistic scenarios of how interest rates can impact a bank’s finances. Unlike traditional forecasting, dynamic forecasting continuously updates data inputs to capture up-to-date changes in customer behavior, interest rates, and other economic conditions.

Using cash flow-based forecasting can be particularly useful to banks as part of a dynamic forecasting framework. This is because it can determine how individual assets and liabilities are expected to react over time. This, in turn, provides more transparency for future earnings, liquidity, and risk exposure.

With dynamic forecasting, banks can also test more scenarios in less time. This allows finance teams to evaluate the impact of multiple interest rate environments, liquidity conditions, and customer deposit behaviors, all without having to build a new model from scratch.

Ultimately, as market conditions or assumptions change, forecasts can easily be updated to reflect the latest information.

The Role of Scenario Modeling in ALM

Scenario modeling plays a key role in ALM as it helps banks see how changes in the market can impact future financial performance. With scenario modeling, banks can test multiple assumptions using various data inputs and then evaluate the outcomes to identify and prepare for risks.

Parallel and non-parallel rate shocks are two common interest rate shock scenarios where rates move at the same or different amounts across the short- and long-term yield curves. Liquidity stress testing can also be done to see if the bank has sufficient liquidity given various deposit scenarios, like deposit migration or other funding disruptions.

Ultimately, scenario modeling can identify how earnings, liquidity, and risk exposure can be impacted with varying sets of assumptions on rates and economic conditions. ALCO teams can use these insights to make faster, more informed decisions and take a more proactive approach to balance sheet and risk management.

Managing Interest Rate and Liquidity Risk Together

Interest rate and liquidity risk are interconnected, as changes in interest rates can impact deposit flows, customer behavior, borrowing activity, and funding costs.

In the context of deposit movement and funding sensitivity, changes in interest rates can cause customers to seek higher returns at competing banks. Losing these deposits could result in less liquidity and fewer funding sources to support lending activity.

Given how these two interact with one another, banks should evaluate them together rather than individually. This integrated approach to balance sheet management can therefore provide a more complete view of the impact of interest rate changes and aid in strengthening contingency planning.

How Community Banks Can Modernize Interest Rate Risk Management

Banks can modernize interest rate risk management by using more efficient processes. Many outdated methods rely on manual, spreadsheet-driven procedures, which can result in inconsistent data, conflicting assumptions, and time-consuming reconciliation efforts.

Recent risk management statistics highlight the growing complexity of financial risk management and the need for faster and more consistent forecasting processes.

Here, modern ALM platforms can meet this need, largely thanks to automated processes. This includes the collection, calculation, management, and analysis of data. Automation also helps ensure that teams across the organization use standardized assumptions.

The result is that teams can spend more time analyzing and evaluating decisions, rather than building or maintaining the model itself. By ensuring all teams are aligned on data and assumptions, results become more reliable, helping institutions strengthen risk management and make decisions with greater confidence.

How Empyrean ALM Supports Dynamic Forecasting

Understanding how to manage interest rate risk in banks requires more than periodic reporting. Rather, banks must be able to regularly identify and evaluate changing conditions before they negatively impact the organization, something that can be done with dynamic forecasting.

Empyrean helps banks move from less reliable static forecasting to a strategic ALM approach with dynamic forecasting. Its platform supports cash flow modeling, integrated scenario analysis, and dynamic balance sheet forecasting. With a unified data model serving as a single source of truth, institutions can evaluate multiple scenarios faster, improve risk visibility, and generate actionable insights for ALCO decision-making.

Learn how Empyrean ALM can help accelerate decision-making and improve risk visibility.

Ready to modernize your interest rate risk management platform? Request a demo today.

FAQ: Interest Rate Risk Management