Much of the discussion around CECL relates to the added compliance requirements needed to implement the new loss forecasting approach. But as with most things in life you can ask 'Is the glass half full or half empty?' Half empty says it's a new burden. The positive take on CECL is that it presents an opportunity to learn more about account behavior with a goal of improving underwriting and account management. With CECL, gone are the days of policy changes driven by anecdotes like the story about the most recent account write-off.

The data needed to develop CECL forecasts already exist in most institutions, either internally or with their third party processors. This includes loan application information, delinquency, loss and payment performance data over time, and recovery amounts. Bringing the pieces together in a structured way supports forecasts at the segment level. Different parts of each portfolio will behave very differently.

Vintage comparisons -- measuring how accounts originated at different points in time -- will help each lender determine whether their policies are leading to better performance. Ideally you'll see improved performance in newer accounts compared to those originated earlier. If the opposite occurs it serves as a warning sign of bigger problems in the portfolio and underwriting or the economy as a whole. It also means you need to take a hard look at how you'll approach your CECL forecast and the assumptions used.

Comparing your portfolio performance -- and segments within the portfolio -- with other similar lenders can also help identify opportunities to improve policies and performance. This is the advantage of pooling your data with other lenders. You won't know the identify of the other institutions in your benchmark; nor will other participants know that you are part of their benchmark.

One of the other big advantages of pooled data is that it helps to 'fill in the blanks' where your data might be thin. By 'thin' we mean a small number of accounts in a given segment. The small number leads to uncertainty. If no accounts have gone bad in a small number it may be due to quality management or just luck. A larger number of accounts in a segment provides greater assurance of an accurate result.

Overall it boils down to what you want to get out of working on CECL. If you minimize the effort you put in and the questions you ask, the value will match. However, if you tackle CECL head on with a goal of learning as much as you can about your products, customers and their performance then you'll strive to build a robust solution that will satisfy CECL and the analytic needs of your business.