Managing risk is at the very core of the business of banking and a fundamental differentiator between financial institutions. In other words, institutions that identify, measure, and manage risk most effectively will outperform their peers in terms of financial performance, while also maintaining safety and soundness. This is especially true during economic downturns as institutions may confront increasing credit risk in their loan portfolios as well as liquidity risk, interest rate risk, and potential pressure to maintain appropriate capital levels.
Concerns about an economic slowdown and a possible change in the business cycle have again put risk management practices in the banking sector back in the spotlight. However, since many smaller institutions are not required to have a formal enterprise risk management program, these practices are often siloed within institutions and reactionary to regulatory pressure (i.e. quickly trying to patch the holes the examiners poke at).
While there are existing best practices to address many of these risks (for example, stress testing to evaluate credit risk, ALM for liquidity, and interest rate risk), FI’s must look beyond evaluating each risk type in a vacuum, and instead account for ways these risks and best practices used to manage them overlap and interact with each other. Only through a more holistic view of their risk management processes can institutions be confident that they have the right information to inform their capital planning, risk appetite, and overall strategy going forward. Financial institution management and directors armed with this information are better able to respond to future challenges and execute their plans – taking on “enough” risk without absorbing “too much.”
Overlap between the silos
In today’s competitive environment, having the right tools and information to make key decisions on operations such as CECL, ALM, and stress testing requires management to have strong analytical tools based on reliable and consistent data. Although connecting processes does not happen frequently, it should, as leveraging a single source of data for the various metrics and analysis helps ensure consistency. For a more holistic risk management effort with consistency across processes, it will be critical for institutions to identify and utilize the common ground. These areas of overlap include:
Underlying data elements
Across all of these processes and best practices for risk management, the starting point is an accurate underlying data set. That data set typically begins with a point in time, loan-level extracts containing detailed term, structure, status, and coding information for all loans in the portfolio as well as detailed deposit information.
For many FIs, the core accounting system is the source of record for this data. However, in many cases, the core may not contain all the information required for a particular process. For example, a bottom-up type stress test typically requires additional, “non-core” data such as credit-focused metrics like NOI, EBITDA, cap rates, and/or collateral values to be useful. This type of information may live in an FI’s credit spreading system, a data warehouse, or may need to be collected manually from credit files. Additionally, externally serviced loan portfolios can create challenges in compiling a complete data set.
Institutions should first look to leverage any existing data extracts they may have created elsewhere in the institution to ensure consistency across these processes, and then attempt to supplement process specific data needs. Loan level extracts for the ALLL are often a good starting point due to the controls and governance required for using the data for financial statement purposes. Additionally, robust coding across loans and deposits enables more consistent application of assumptions across categories and eases some burdens of financial reporting.
Modeling assumptions & inputs
As previously noted, the alignment of critical assumptions and inputs is fundamental to a holistic view of risk management for FIs. Regulators have also been clear that they expect consistency across these various processes and may require explanations when they deviate. Assumptions should be institution-specific, if possible. When institution-specific information otherwise strains credulity, question the strength of the assumption and the weight of the evidence leading to it. The key thing is to evaluate this at an input-by-input level. If an institution has a weak input without a weight of belief behind it, they can leverage market or peer-based information. However, some of the critical institution-specific assumptions that should be analyzed first include:
Prepayments and early withdrawals: Expectations of deviations from contractual terms for both loans and deposits are critical for ALM modeling but are also needed in any discounted cash flow (DCF) based modeling (CECL or portfolio simulation type stress tests).
Average life expectations: How long do we expect different loan types to stay on the books relative to their contractual term? It may reflect prepayment expectations as noted above, but in some cases, it might be used as an input for non-DCF based models (particularly for ALLL/CECL).
Credit risk assumptions: These can be expressed in different ways (i.e. loss rates vs. PD/LGD), but assumptions for credit losses across various loan types is fundamental to virtually all risk management models. Again, institution-specific assumptions are typically preferable to market or peer-based measures, however, many FIs may need to look outside of their institution due to the low loss environment of recent years.
Regardless of how they are derived, credit risk assumptions should be consistent throughout the entire risk management process. For example, an institution may leverage sets of PDs/LGDs in estimating their allowance under CECL while also using them to quantify the impact of changing conditions and the costs related to downgraded credits under various stress testing scenarios.
Forecasts: These include both economic forecasts (such as outlooks for GDP and unemployment), as well as institution-specific forecasts (such as loan growth and funding costs). Consistency remains critical, as these forecasts will be used across multiple processes. Support and documentation for how these forecasts are sourced and how they specifically, or in some cases statistically, relate to changes in the balance sheet is also important.
As noted above, it is important in some cases to distinguish between forecasts (expectations about what will happen) and scenarios (examples of what could happen). For example, using the DFAST scenarios of economic conditions (baseline, adverse, severely adverse) makes sense in a stress test/portfolio simulations but likely would not be appropriate as part of a reserve estimate under CECL as the scenarios do not represent the institution’s expectation of future conditions.
The case for automation
There is a host of data required for the various risk management processes (with significant overlap). Automation can provide significant benefits around data governance and management and reduce the amount of “data duplication” seen with many community FIs.
Effective stress testing and ALM programs employ a range of scenarios that management can then evaluate and compare. Automated systems can enable easier and faster scenario generation while limiting the manual (and error-prone) nature of spreadsheet modeling.
Different risk management processes living in a connected, automated environment can leverage common data sources, ensure consistency across various model inputs and assumptions and provide powerful reporting capabilities.
About the Author
Tim McPeak assists is an executive risk management consultant at Abrigo, where he advises on risk and portfolio management with financial institutions nationwide. Previous to his current position, Tim led Abrigo’s strategic partnership program, through which the company partners with consulting, loan review, accounting, and other professional services firms. Before joining Abrigo in 2011, Tim spent several years as an associate with investment banking firm Babcock & Brown, focusing on commercial real estate and infrastructure finance. Tim began his career in retail and business banking with Key Bank of New York. He received his bachelor’s degree from Wake Forest University.