The Biggest Initial Effect of CECL on Financial Institutions

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Most financial institutions understand Accounting Standards Update (ASU) 2016-13, Financial Instruments—Credit Losses, and—more specifically—applying the current expected credit loss (CECL) model to their loan portfolio represents the most significant accounting change for financial institutions in recent memory. However, there’s less certainty on how the standard will specifically affect each institution. The conversation on its effect goes well beyond day-one increases in allowance. Though there are myriad potential effects of the standard, one of the most substantial and immediate is data. Many institutions we work with, especially those in the early stages of CECL implementation, are uncertain about why or how data becomes such a significant issue. To provide clarity on those concerns, we‘ve broken down data issues we’ve encountered in real-life CECL implementations into the following four main areas:

  • What you don’t have

  • What you do have

  • How you got it

  • How you’ll maintain it going forward

Before we dive into each area, a word of caution: Each institution’s implementation is unique, and the complexity of data needs and integrity is a product of many variables. These variables include pooling segmentation decisions, models/methodologies being considered, strength of internal controls in your institution and how you currently store and maintain historical data, to name a few. Therefore, you may not experience all of the issues described below, but you’ll likely face challenges related to data.

What You Don’t Have

Data issues in this area usually are the easiest for institutions to grasp, and many of these reveal themselves quickly in the process. These relate to data fields you don’t have or don’t consistently capture. For example, an institution thinks loan-to-value or debt service coverage ratio is a good indicator of future expected credit loss in commercial loans and, therefore, incorporating these into its CECL model may make sense. However, the institution quickly realizes it either doesn’t capture those data points in its loan systems or doesn’t regularly and consistently update them. Therefore—without substantial cost to the institution—these data points can’t be obtained. This typically leads institutions to consider other data fields, such as loan grading, that may be more reliable and still incorporate those factors. Other common issues in this area include:

  • Inability to access historical data after a certain time frame

  • Lack of quality historical data from acquired institutions

  • Data points for future forecasts

What You Do Have

CECL is forcing many institutions to reconsider the quality of data they obtain in their loan systems and data warehouses. As institutions consider integrating more or different data in their CECL models, many find themselves asking, “How comfortable am I with the completeness and accuracy of the data?” Common issues we’ve seen in this area include:

  • Accuracy and consistency of how data is input and recorded into loan systems

  • Changes in underwriting or grading systems causing lack of consistency in historical data sets

Specific problem areas include how loan renewals and modifications are input, loan payment fields being recorded as a whole rather than separate principal and interest amounts and lack of detail for charge-offs to determine how much was principal versus remaining premium or discount or other components of amortized cost basis. Institutions should document how they’ve concluded the information in their loan systems is accurate by assessing the controls over input and maintenance of data fields necessary in their CECL models/methodologies. To help get auditors and regulators comfortable with these conclusions, it may help to reference how internal audit and controls testing provides management with independent validation of the accuracy. If certain data fields haven’t been consistently tested or validated, or have shown a history of issues, then additional testing may be necessary to gain comfort over the reliability of those data points. 

How You Got It

Institutions that have made progress in their CECL implementation quickly realized they’ll need to obtain and store loan-level data in a different manner, and for a longer period of time, to implement CECL compared to today’s incurred loss approach. While some institutions already have data warehouses, many others are creating data warehouses of historical loan-level data to address this issue. Those with existing data warehouses, especially if their external auditors already are auditing the controls over the completeness and accuracy of the data warehouse, should have a much lower risk of data gaps or errors in this area. However, for those creating a new data warehouse for CECL, issues can arise if validation isn’t performed over the completeness and accuracy of the data uploaded into the data warehouse. This will require management to establish internal controls over the completeness and accuracy of uploading data into the data warehouse.

How You’ll Maintain It Going Forward

As noted above, many institutions have realized they need to create data warehouses to store historical data sets outside their existing loan systems to leverage that data for CECL. This presents the challenge of how the integrity of the stored data will be maintained and protected going forward. If an institution is considering maintaining an in-house data warehouse in Excel as a long-term solution, then it should develop robust spreadsheet controls to protect the data from accidently or intentionally being manipulated. Implementing these controls can be a complex process and may lead institutions to seek alternatives. Institutions that are moving to an outsourced vendor solution that includes their data warehouse will likely have less to worry about, since those vendors should have well-defined controls and control testing over data integrity. No matter what avenue an institution takes, it should assess what could go wrong in the process of uploading new data and maintaining existing data in its data warehouses and make sure it has adequate controls to cover any significant risks.

Conclusion

The ways data can become a barrier to CECL implementation are too numerous to list. A best practice to help a financial institution overcome these barriers is to evaluate what you don’t have, what you do have, how you got it and how you’ll maintain it going forward. To complete this exercise, institutions will need adequate education of the CECL standard, a thorough understanding of potential models and methodologies and a general idea of how they’ll pool their loans under CECL. By taking time to think through your responses to these areas, you can work to determine your data gaps and decide what action needs to be taken.

For assistance with CECL implementation, contact Andrew, Gordon or your trusted BKD advisor.


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