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Managing Your Credit Union’s Loan Data

November 8, 2015
Read Time: 0 min
Whether it’s part of a CECL preparedness conversation or part of a more proactive approach to risk management under existing regulatory expectations, the topic of “loan-level data” has repeatedly come up since the 2012 proposal from the FASB. As a result, at Abrigo, we have received many questions from our clients – banks and credit unions alike – about the steps to data preparedness.

Credit unions face a distinct challenge in that, generally, borrower data for a credit union is stored in several different core processing or decisioning systems. These data silos make it all the harder for credit unions to begin data archiving. There are more sources from which to pull information and, probably, fewer IT resources that can focus on data management at a credit union.

Abrigo helps our clients overcome this data challenge through a customized core integration, but how can a credit union gather loan-level data?

Limited Method

For CECL specifically, it’s likely that a credit union will need several years of data (life of loan) to accommodate the forward-looking calculations. One way to capture this information is the Limited Method, in which the credit union uses data already stored in its core and decisioning system(s). Often these systems store data for up to 13 months, so look into your own core provider to see the limitations it may present. For this and the following methods, the credit union will likely have to have access to a report writer or know how to access core information.

Static Method

The next option is similar to the first but it takes data collection a step further. With the Static Method, the credit union captures one-time archives of data from each of the different core(s). Then these archives are stored in an accessible format so that, through spreadsheets, the risk management team at the credit union could manipulate the data for insights on the portfolio. With this model, the credit union takes on responsibility for pulling the archives as well as storage of the data in a query-able format. For example, PDFs with data tables probably will not help the credit union achieve its reporting goals.

Dynamic Method

In this model, the credit union will use a provider like Abrigo to create a data bridge between the institution’s core data and an ALLL solution. This method certainly introduces the most change for the credit union so it may or may not be appropriate for each institution.

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Once the credit union can, using one of these methods, access historical loan-level data on its portfolio, there are a few added benefits that the risk management team can leverage:

  • More defensible, documented calculations
  • Easier process for balance reconciliation as part of the ALLL
  • Less subjectivity in forward-looking assumptions under CECL
  • Ability to enhance the ALLL with more sophisticated loss rate calculations including migration analysis or PD/LGD
  • Opportunity to create different ALLL (and stress testing) scenarios to see the impact model tweaks might have on the reserve – this includes testing how CECL will impact capital
  • Backtesting to validate the model’s accuracy over time
  • Better portfolio reporting to understand risk

Portfolio reporting with loan-level data can be extended to cover:

Historical Balances

Loan-level detail for the portfolio enables a credit union to track the movement of loans from each segment to determine how the portfolio is changing. How is the portfolio shifting? Is there substantial growth in one concentration? Should the credit union shift focus to another area?

Asset Quality

Similarly, delinquency rates by concentration will give greater clarity into where portfolio risk may be increasing. If these pockets of risk are uncovered, the credit union can do something (e.g., change loan pricing models, adjust risk appetites, better monitor TDR activity) to mitigate risk moving forward.

The credit union can also review loans by segment to see which loans provide the best chance of obtaining a recovery based on historical data. This allows the institution to potentially reallocate resources including workout officers or special assets officers to maximize dollars returned to the credit union.

About the Author


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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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.

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