Credit unions have long been in the business of managing credit risk, so it comes as no surprise that innovative institutions are finding better ways to use data to perform risk assessments. A probability of default model (PDM) is one way that credit unions can use financial information about a member business to anticipate future risk in the member's ability to service debt. Probability default models can thereby equip institutions to more proactively identify and mitigate would-be "bad" loans, and examiners and auditors applaud the objectivity and documentation in such an analysis.
For member business lending departments, understanding credit risk can be a cumbersome and detailed task, including:
- Knowing what data to collect from the member (and its guarantors)
- Spreading the information
- Adjusting for commingled debt and income to arrive at a global cash flow assessment
- Benchmarking to other businesses in the industry
- Understanding how the proposed loan impacts creditworthiness
Making the process more difficult, for many credit unions, this process is inconsistent, with individual lenders making nuanced changes to the process, which introduces risk for the institution in the eyes of examiners and auditors.
The good news is that this process, by and large, works. Credit unions continue to make good loans to members, helping to grow their local communities and better servicing their customer base.
But if given the option and the resources, wouldn't a credit union prefer to go beyond making "good" loan decisions to make even "smarter" ones?
The advent of big data has given progressive credit unions a lot of "predictive ammo" to understand their members, predict who else might be a good customer, predict what product offerings to market to them, etc.
However, even with "little" data - some basic, financial performance metrics about the member business - it's possible for credit unions to predict potential future losses in borrowers, using a PDM. This objective, documented and forward-looking measure of risk can supplement the credit union's existing underwriting and review expertise to help make loan decisions even smarter.
Depending on the specific PDM that is implemented in the credit union, the information that's required will vary, and its application may be different as well. However, a PDM can provide management a tool with which they may perform a deeper and more objective analysis when making individual credit decisions. This can include:
- Pre-screening members, without investing the time and resources required for a full spread
- Substantiating credit analyses with an automated and predictive measure of risk, especially for new relationships
- Reducing the weight that more subjective risk measures carry in risk ratings
- Systematizing and automating, in some cases, the risk rating for loans and delineating between loans that often pool in one or two risk ratings
- Understanding the global probability of default
- Guiding loan pricing to more closely reflect risk
- Identifying loans or prospective loans that may need tighter covenants or administrative controls put in place
- Simplifying annual loan review for applicable loans to save time for the loan officers or administrators
- More quickly identifying loans that may in time default, giving the relationship manager (loan officer, analyst, etc.) more time to right the situation
- Providing an added layer of quantifiable and defensible risk measurement, which could make for easier exam and audit relationships
If a credit union has recently invested resources into a member business lending program or is attempting to grow that part of the portfolio, the systemization, efficiency and accuracy provided by a PDM could strengthen the department and ready the institution to continue growing.
A sound PDM offers a credit union many benefits, providing additional fortification against credit risk and giving the institution more objective information with which to understand portfolio health. While implementing a PDM may require process changes, it can be first added as a supplemental step in underwritings, helping the credit union make smarter lending decisions as the portfolio grows.
Libby Bierman is a financial analyst at Sageworks, Raleigh, N.C.
