CFS Loan Risk Model

Every Credit Union makes a considerable investment in trying to understand the risk of a loan before origination. However, the risk of a loan is not static. Measuring the change in risk can be very challenging. Some Credit Unions will address this by collecting period credit scores and checking to see if there was a change. This option has several limitations in that it’s expensive, slow, and is not loan specific in scoring the risk.

The CFS Risk model looks at the payment behavior of every collateralized loan to assess the risk for that specific loan. This results in the following advantages:

  • The risk score is more timely / accurate as it based upon the borrower’s most recent loan payment.
  • Since the risk is based on their payment behavior on each loan vs. a credit score that could be as much as six months behind in reporting.
  • The model is sensitive to both the timing and amount of payments and is influenced by differences between the expected and the actual payment.

Because of the unique way that the risk is assessed the value can be used very effectively in predicting which loans are more likely to charge off. This allows the Credit Union to improve the reserve value for the loan portfolio in addition to adjusting tactics in collections for those loans at the highest risk level.

Common Use Cases

  • Optimize collection efforts toward those loans that carry the greatest risk
  • Improved accuracy in calculating reserve amount required for potential loan losses.
  • Improve ability and reduce effort in responding to examiners’ questions on accounting for loan loss

Common Questions

Only collateralized loans are included in the model.

Learn more about the CFS Insight Loan Risk Model here

The model runs once a month which gives each loan a chance to make a payment in that period.

Learn more about the CFS Insight Loan Risk Model here

CFS uses Python to create, maintain, and execute the models.

Learn more about the CFS Insight Loan Risk Model here

Yes, the data is captured monthly and stored. This allows the CU to look not only at the risk for a specific loan but also assess the risk of the portfolio over time.

Learn more about the CFS Insight Loan Risk Model here

Technical Details

  • Runs on SQL Server 2014/2016/2019 Standard Edition or Enterprise Edition
  • Requires access to Detailed loan balance data
  • Requires access to 3 years of loan payment history as well as loan payment history going forward.

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Data connectors and CFS analytical models are just tools. To succeed, your data initiative needs the right process to handle the preparation, detailed integration, and well planned training and support. The CFS process will help guide your project to successful implementation and adoption.

CFS Insight Process

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