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Data & Analytics Portfolio Risk & CECL Allowance Calculation CECL Models Stress Testing

Maximizing CECL data for strategic insights

Justin Crawley
February 21, 2025
0 min read
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Making the most of data developed for CECL 

See how banks, credit unions, and other financial institutions can leverage data developed and used for the CECL model for stress testing and strategic insight. 

Key topics covered in this post: 

5 ways to use data developed for the expected credit loss model

As CECL was first being discussed, a primary worry for many financial institutions was that the  collection, storage, and use of the necessary data to produce a sound CECL calculation could place a burden on an institution attempting to comply with the new standard.

Here we are in 2025, and that concern is being handled well by financial institutions of all shapes and sizes, with many opting to partner with various CECL software vendors, such as Abrigo, to ease the associated burden. As is often the case, what was initially a concern about data has resulted in opportunity.

Learn more in this webinar, "Transforming CECL data into stress testing and strategic insight."

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Re-using CECL data across the institution

Financial institutions are leveraging the data collected and used for the current expected credit loss model (CECL) to meet other needs for strategic information inside the institution. Here are five ways financial institutions can make the most of their CECL data to help with competitive positioning, more effective pricing, asset/liability management (ALM), and other decision-making:

Peer analysis and comparison

We often categorize data into two types: raw/input data and the output, or enriched data. For many of the institutions that our Advisory group works with (and other institutions finding their solution outside of Abrigo), one output of a meaningful CECL calculation is the metric-based peer groups built to supplement historical data. The development of these CECL peer groups, while necessary for the task at hand, has also provided the opportunity for institutions to generate comparisons using a group that often is far more relevant and appropriate than any peer groups they used before CECL. The ability to measure performance against a suitable peer group is vital to understanding an institution’s relative place in its own market. These comparisons can be useful for decision makers at every level.

Rate studies

Understanding how the output described above can be helpful aside from CECL, let’s look at uses for certain input data beyond calculating the allowance. After all, building relevant CECL models using an institution’s own data is generally preferred to relying on peer-based studies. Having the loan-level data necessary for a CECL calculation all in one place opens opportunities for financial institution managers to glean statistically relevant observations of portfolio performance over time. For example, the loan files typically give us the data we need to produce prepayment rate studies: observations of balance changes period over period. These studies often impact the CECL results themselves, but they can be useful in other areas like pricing and ALM as well.

Economic stress testing

It’s important to fully understand a CECL model and be able to predict the model performance in varying economic conditions to allow managers to anticipate needed accounting entries. Economic stress testing is made much simpler when a CECL calculation exists in the same system to leverage common inputs. Whether it is for adherence to DFAST or an institution’s desire to gather deeper understanding of their model’s potential, having CECL and stress testing available together to play off each other is an efficiency directly resulting from the data gathering required for CECL models.

Business intelligence and insights

The same files that come into play for a meaningful CECL calculation can be used to produce insightful business intelligence for a financial institution.  Advanced banking intelligence tools like Abrigo’s Connect can produce, for example, past due trend analyses that give visibility into portfolio performance that wouldn’t be possible without the data sets and time series data needed for CECL. There is power in generative AI tools that can analyze data and give access to the results financial institutions otherwise would have likely missed.

Another example of intelligence is heat mapping data that has been generated related to both collateral and borrower files (all part of a CECL data stream). Financial institutions serving areas hit by the California wildfires of this year have been able to leverage these data files within Connect to identify areas impacted within their portfolio. They have been able to cross reference the heat maps to the government databases of wildfire impact to respond more quickly. The data can help them offer better service to their borrowers and begin anticipating the negative impact of the fires on their unique loan portfolios.

What-if and trend analysis

This is a bit of a subcategory of point #4, but a quality CECL tool with the datasets required for a calculation can provide insight that can’t easily be replicated outside of a platform of its kind. For example, probability of default trend analyses are produced as part of certain methodologies used in creating a CECL calculation. But they also offer insights to credit teams who are generally not even involved in CECL calculations. The observations of historical trends created for CECL allow credit risk managers to identify what may happen given projections in real time. As a result, the financial institution enhances its ability to anticipate events rather than simply react to them. Coupled with additional analysis, the influence of these trends can guide credit teams in pricing loans and other related activities.

Use CECL inputs & outputs opportunistically

CECL was certainly not a welcome change for every institution required to adopt it, but the examples above point to some of the potential fruits you can find from your labor. The data institutions have gathered and stored can be used opportunistically by engaged and willing financial institutions to impact areas of the institution outside of risk management alone. All it takes is a little ingenuity and the willingness to alter your perspective a little bit.

What are the drivers of credit loss at your institution? Let our CECL consulting team help you find out.

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About the Author

Justin Crawley

Senior Consultant
Abrigo
Justin Crawley is a Senior Consultant with Abrigo Advisory Services, where he provides subject matter expertise on CECL and stress testing. He joined Abrigo in 2019, helping dozens of credit unions and banks transition their allowance methodologies and align with best practices surrounding the current expected credit losses (CECL) accounting

Full Bio

About Abrigo

Abrigo enables U.S. financial institutions to support their communities through technology that fights financial crime, grows loans and deposits, and optimizes risk. Abrigo's platform centralizes the institution's data, creates a digital user experience, ensures compliance, and delivers efficiency for scale and profitable growth.

Make Big Things Happen.