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CECL Q&A – Data

Sageworks
February 24, 2017
Read Time: 0 min

The FASB’s Current Expected Credit Loss (CECL) model presents unique challenges for banking professionals. To help institutions prepare, Abrigo launched a CECL webinar series covering data, segmentation, methodology, and forecasting requirements broken down by loan pool-type. A key component of the series is allowing participants to ask their CECL-related questions, and below are several questions and answers relating to data requirements.

Questions related to the amount and type of historical data:

How much historical data is required?
At the minimum, if you are not going to do a Discounted Cash Flow (DFC) calculation, you need to have historical data covering the life of the asset. If you want less volatility and clarity into your forecast, you will want more data.

How many historical periods do I need to include; do they need to be stored monthly, quarterly, or annually?
Ideally, this data would be stored on a monthly basis for the life of the assets. For example, to calculate a 3-year loss experience and assuming your implementation date as an SEC-filer is December 31, 2019, then you will need to have data from the 12 previous quarters or from Q1 2017.

We have call report data going back N years; do we have enough data?
Summing annual loss rates is punitive because you also have losses in the numerator of your calculations. Looking at just annual average loss data does not provide you with enough data to calculate a life-of-loan loss rate.

We have loan number, balance, and loan term information stored; is this what you’re referring to?
This might be sufficient for one method such as Vintage, but as the characteristics of your loan pools change so will your methodology and so will the data that is needed to calculate. In addition to loan number, balance, and loan term, we encourage institutions to capture, as a point in time, the risk metrics associated with a loan and not just the basic loan information.

We have origination information, loan number, loan balance, and loan-level charge-off data; what else are we missing?
Similar to the previous question, to retain flexibility, limit volatility and increase your understanding of the analysis, we suggest having the risk metrics associated with the loan information at your disposal.

If you are interested in more answers to CECL, watch the on-demand webinar, CECL Methodology Q&A.

About the Author

Sageworks

Raleigh, N.C.-based Sageworks, a leading provider of lending, credit risk, and portfolio risk software that enables banks and credit unions to efficiently grow and improve the borrower experience, was founded in 1998. Using its platform, Sageworks analyzed over 11.5 million loans, aggregated the corresponding loan data, and created the largest

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