Hindsight is 20/20, but That’s Not Enough for CECL
The current expected credit loss standard (CECL) will require financial institutions book loan loss allowances for the life of the loan at the time of origination, and—if that changes over time—an institution’s income can take a hit.
To accurately predict loss allowance, especially for loans with a longer lifecycle, banks and credit unions will need sufficient data for CECL implementation.
Since this data informs how the loan is predicted to perform, it is crucial that financial institutions capture the right data at origination, identify data that is still needed, and use data to track loan portfolio performance.
In this post, we’ll explore how you can prepare for CECL data requirements before the 2020 deadline to ensure CECL does not negatively impact the operations of your community bank or credit union.
What Data Do You Have?
To prepare for CECL, understanding what data is currently available (and where it is located) should be a top priority for financial institutions. This task should not be the burden of one department or one employee. Instead, it should be the responsibility of multiple stakeholders from across your institution, such as accounting, finance, IT, and credit.
Each of your employees can offer insights about accessing and collecting the right data from their respective areas. Gathering and organizing fundamental data—like historical defaults, attrition and recovery data, collateral information, prepayment and delinquency data, as well as macroeconomic variables—is a good place to start.
On a go-forward basis, this team will work together to identify other elements that should be factored into your institution’s loss estimation models.
How Is Your Data Organized?
When it comes to organizing data, financial institutions should ensure data is integrated across business lines by eliminating disparate silos and spreadsheets of information.
Now is also a good time to address inconsistencies in data variables because discrepancies might hurt your institution’s ability to effectively segment data and determine what influences loan portfolio performance.
To avoid inconsistencies when inputting data, establish a standard protocol. For instance, does your institution include prefixes when entering a borrower’s name? For addresses, are abbreviations like “Ave.” used consistently or are they spelled out? Whatever your institution prefers, make sure everyone is aware of—and adheres to—the same standard.
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Community banks and credit unions should also evaluate their loan origination processes and take steps to analyze credit requests with greater consistency.
To do this, loan applications should be evaluated based on your institution’s credit policies and underwriters should be able to gain a complete view of the applicant through relevant data. This can help your institution better understand how loans are being decisioned, how they are being priced, and, ultimately, what drives their performance.
Additionally, after a new loan is booked, regularly collecting data points that indicate credit quality will be important. Reviewing indicators—like FICO scores, loan to value ratios, and debt coverage ratios—on an ongoing basis will enable your financial institution to track the quality of the credit as it changes and provide insight as to what is causing those changes.
Analyzing greater volumes and more types of data will support forward-looking loss estimates that can be reliably predicted, ensuring an optimally-sized reserve with minimal income volatility.
While hindsight is 20/20, with the right data your financial institution can gain a clearer view of the future and prepare accordingly.
Posted on Friday, August 31, 2018 at 2:00 PM
by John Robertson