Paragraph 326-20-30-3 of the Financial Accounting Standards Board (FASB) standards update states: “The allowance for credit losses may be determined using various methods”. I’m not sure if any statement, other than “We need to talk”, can be as fear inducing. Why is it scary? Because in the world of details and accuracy, this statement is remarkably vague and not prescriptive.
Below are some questions to consider when determining the appropriate loss methodology approaches for a given segment.
How much history do you have?
If a financial institution (FI) has limited history then the options available to them are, well, limited. To build a model one needs sufficient data to capture the behavior (e.g., performance or payment) of accounts. Without enough data the probability of successfully building a model is low. Worse yet, even if one builds a model, the likelihood of it being useful and robust is minimal. As a result, loss methodology approaches that do not need a lot of data should be considered (e.g., discount cashflow or a qualitative factor approach based on industry information).
Have relevant business definitions been created?
The loss component approach (decomposing loss into PD, LGD, and EAD) is considered a leading practice at banks. However, in order to use this approach definitions of default and, arguably, paid-in-full, need to be created for each segment being modeled. (Note: these definitions can be the same or different across segments.) Without these definitions, one does not know when an account has defaulted or paid-off.
Is there a sufficient number of losses or defaults in the data?
Many of the loss methodologies available for consideration (e.g., loss component or vintage loss rates) require enough losses to discern a pattern. As a result, banks that are blessed with infrequent losses can feel cursed when they try to implement one of those approaches. While low losses do not necessarily rule out these approaches, it does make for a more challenging process.
Are loan level attributes available, accurate, and updated appropriately?
This question tackles the granularity of an approach instead of an approach itself. As mentioned in the post CECL – Data (As Usual) Drives Everything [link], there are three different data granularity levels a model can be built on. Typically, the decision is between loan-level versus segment level. Loan-level models are great for capturing sensitivities to loan characteristics and macroeconomic events provided the loan characteristics are accurate and updated (if needed) on a regular interval.
Jonathan Leonardelli, FRM, Director of Business Analytics for the Financial Risk Group, leads the group responsible for model development, data science, documentation, testing, and training. He has over 15 years’ experience in the area of financial risk.
 There is no consistent rule, at least that I’m aware of, that defines “limited history”. That said, we typically look for clean data reaching back through an economic cycle.
 See: Capital Planning at Large Bank Holding Companies: Supervisory Expectations and Range of Current Practice August 2013