Taking Model Risk Management to the Next Level

A financial institution’s data is one of its most valuable resources. Banks constantly collect data on their loans, deposits and customer behaviors. This data should play a key role in how financial intuitions manage their risks.

Yet, developing a data strategy can be seen as too complex based on the sheer amount of data an institution may have, or as an unnecessary burden if the objective is solely to use the information to satisfy regulatory requirements. But a holistic data strategy can enhance value across all model risk management (MRM) platforms, both for regulatory and strategic purposes. On the flip side, being inconsistent or not updating data and inputs in a timely manner can lead to inaccurate or inconsistent results. Executives need to continually update and review information for consistency; if not, the information’s relevancy in assessing risk across various platforms will decrease.

Currently, the most common data strategy approach for banks is using individual tools to measure risk for regulatory purposes. For instance, financial institutions are required to calculate and monitor interest rate risk related to their balance sheet and potential movements in future interest rates. Typically, one team within the institution extracts data and transfers it to another team, which loads the data into an internal or external model to calculate the various interest rate profiles for management to analyze and make decisions. The institution repeats this process for its other models (credit, capital adequacy, liquidity, budgeting, etc.), adjusting the inputs and tools as needed. Often, banks view these models as individual silos — the teams responsible for them, and the inputs and processes, are separate from one another. However, the various models used to measure risk share many commonalities and, in many aspects, are interdependent.

Integrating model risk management processes require understanding a bank’s current data sources and aggregation processes across all of its current models. The first step for executives is to understand what data is currently used across these platforms, and how your organization can utilize it other beyond just checking the regulatory box. In order to enhance data quality, can one data extract be used for multiple platforms? For example, can the same loan-level data file be used for different models that use similar inputs such as asset liability management (ALM) and certain CECL models? While models may utilize some different or additional fields and inputs, there are many fields — such as contractual data or loan prepayment assumptions — that are consistent across models. Extracting the data once and using it for multiple platforms allows institutions to minimize the risk of inaccurate or faulty data.

From here, bank executives can develop a centralized assumption set that can be modeled across all platforms to ensure consistency and align results between models. For instance, are the credit assumptions that are developed for CECL purposes consistent with those used to calculate your ALM and liquidity profile under various scenarios? Are prepayment assumptions generated within the ALM model also incorporated into your CECL estimate? Synchronizing assumptions can provide more accurate and realistic results across all platforms. The MRM dashboard is a tool that can be configured to alert bank executives of emerging risks and ensure that data shared by different models is consistent.

One common method of gaining insights using MRM is through scenario and stress testing. Today’s environment is uncertain; executives should not make future decisions without in-depth analysis. They can develop scenarios for potential growth opportunities, modeling through the integrated platforms to calculate impacts to profitability and credit and interest rate risk. Similarly, they can expand deposit data and assumptions to assess high-risk scenarios or future liquidity issues apart from normal day-to-day operations. Whatever the strategy may be, assessing risk on an integrated basis allows management to gain a better understanding of all impacts of future strategies and make stronger business decisions.

Once institutions begin centralizing their data and model inputs and streamlining their monitoring processes using MRM dashboards, management can shift their focus to value-added opportunities that go beyond compliance and support the strategic vision of the institution.