FASB's guidance for estimating expected credit losses, ASU 326, is not definitive in its requirements for credit unions' allowance for loan and lease losses. The good news, then, is that it is unlikely NCUA examiners will ask for immediate answers as to how your credit union will calculate the reserve under CECL.

However, even if methodology decisions are a future task for credit unions, institutions do need to tackle loan-level data collection now as a first step toward compliance under future GAAP.

Data adequacy starts with evaluating data-collection methods available to the credit union, determining the quality of data that is being collected and filling any gaps that are identified.

Data Collection Methods

Generally, credit unions will look to one of three methods for collecting and archiving loan information.

Considerations for Each Method

1. Limited Method: In this model, the credit union leverages its core system(s) to capture data. Most likely, it is not a viable approach for most core systems due to limited data storage, usually a set number of trailing months or quarters.

In my experience, it can be common for a credit union to store loan data in different places – mortgages in one system, auto loans in another, etc. While that practice may streamline lending functions, it can make it more difficult for the credit union to coordinate with all of its database providers on archiving limits, if there are any.

2. Static Method: Here the credit union builds an internal database and saves data from its core system(s) periodically, or saves month-end “trial balances” in Excel worksheets. This option preserves flexibility for the credit union later in the project as the institution can store as much or as little data as it desires. However, the static method might require IT resources that the institution does not have. Can the credit union's IT team write reports from all the data sources? Is there a secure yet accessible place to store those files? How easily can this data be analyzed? The institution will also have to consider consistency and coherency of the data, perhaps building a data dictionary to prescribe formatting, labeling and file maintenance.

3. Dynamic Method: In this option, the credit union partners with a technology vendor to refine data aggregation policies and develop the archiving system for storing data. Utilizing this model, the credit union could reduce risk by collaborating with an experienced vendor, and the institution could maintain flexibility for reporting and scenario building.

For each of these options, credit unions can also run a series of tests on their loan data to ensure the information going into the archive will be meaningful and usable in future for CECL calculations. By using some of these suggestions, credit unions can avoid “garbage in, garbage out.”

Data Adequacy Checklist

The data is labeled meaningfully and consistently;
The data file does not contain duplicate fields, rows, identifiers or entities;
There are no inconsistencies in how values are truncated (if at all);
Data is stored in the right format for calculations;
Files extracted from the core system are stored as the right file type and stored in the right, secure location;
File creation is automated instead of a manual process;
Data is reliable and standardized throughout the institution;
Data fields are standardized and governed to ensure consistency;
Data storage does not have an archiving time limit;
Data files are in an accessible format (not PDF); and
The archiving function captures all the data points required to perform a range of robust CECL methodologies.

Filling Data Gaps

If credit unions identify particular gaps, there are a few options from which credit union management can choose in order to resolve. First, institutions can partner with their core provider(s) and other data warehouse providers to modify storage limits. Secondly, they can build internal systems that meet these data-capture needs and bridge credit union departments. Or, they can work with an ALLL automation provider to optimize their loan-data practices.

Danny Sharman is a risk management consultant for Sageworks. He can be reached at 984-242-2614 or danny.sharman@sageworks.com.

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How to Establish Data Adequacy Ahead of CECL

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