To appropriately prepare for CECL a financial institution (FI) must have a hard heart-to-heart with itself about its data. Almost always, simply collecting data in a worksheet, reviewing it for gaps, and then giving it the thumbs up is insufficient.
Data drives all parts of the CECL process. The sections below, by no means exhaustive, provide key areas where your data, simply being by your data, constrains your options.
Paragraph 326-20-30-2 of the Financial Accounting Standards Board (FASB) standards update states: “An entity shall measure expected credit losses of financial assets on a collective (pool) basis when similar risk characteristic(s) exist.” It then points to paragraph 326-20-55-5 which provides examples of risk characteristics, some of which are: risk rating, financial asset type, and geographical location.
Suggestion: prior to reviewing your data consider what risk profiles are in your portfolio. After that, review your data to see if it can adequately capture those risk profiles. As part of that process consider reviewing:
- Frequency of missing values in important variables
- Consistency in values of variables
- Definitional consistency
The FASB standard update does not provide guidance as to which methodologies to use. That decision is entirely up to the FI. However, the methodologies that are available to the FI are limited by the data it has. For example, if an FI has limited history then any of the methodologies that are rooted in historical behavior (e.g., vintage analysis or loss component) are likely out of the question.
Suggestion: review the historical data and ask yourself these questions: 1) do I have sufficient data to capture the behavior for a given risk profile?; 2) is my historical data of good quality?; 3) are there gaps in my history?
Granularity of Model
Expected credit loss can be determined on three different levels of granularity: loan, segment (i.e., risk profile), and portfolio. Each granularity level has a set of pros and cons but which level an FI can use depends on the data.
Suggestion: review variables that are account specific (e.g., loan-to-value, credit score, number of accounts with institution) and ask yourself: are the sources of these variables reliable? Do they get refreshed often enough to capture changes in customer or macroeconomic environment behavior?
Hopefully, this post has started you critically thinking about your data. While data review might seem daunting, I cannot stress enough—it’s needed, it’s critical, it’s worth the effort.
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.
 More on what these mean in a future blog post
 Paragraph 326-20-30-3
 A future blog post will cover some questions to ask to guide in this decision.