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CFS Analytic Models

Today you will hear considerable buzz about artificial intelligence and machine learning, but how specifically do those technologies/concepts specifically allow your credit union to make better decisions, grow and improve member engagement? CFS Insight’s Analytic Models leverage the concepts within artificial intelligence and machine learning in practical ways that your team can understand and implement. We have models that:

  • Illuminate actions that your credit union can utilize to transition members from being casually acquainted to long term loyalists
  • Provide a more current and accurate view of your loan portfolio’s risk and anticipated revenue stream that enables more proactive decision making and action to improve the health and size of your loan portfolio all at the same time.
  • Leverage Geospatial analytics and external data sets that enable your credit union to invest wisely in branch and member growth initiatives.
  • Illuminate members that exhibit unhealthy or risky financial behavior upon which your credit union can better advise and consult to improve the member’s financial outlook as well as curb the risk to the credit union should they not heed your advice.

To get more information about these truly innovative yet practical solutions, please click on one of the models below.

CFS Analytic Models Products


Product Categories

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CFS Analytics Data Connector

CFS Insight’s Analytics Data Connector is the foundation for its analytic models. The creation of an enterprise class member record along with the transformation, cleansing and classification of member financial transactions provides the basis for behavioral, policy and risk analytics. It also provides a look at the credit union’s competitive landscape that surfaces Insights into how to to build deeper relationships with members.

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CFS Member Retention Model

CFS Member Retention Analytic Model leverages near real time analytics to create retention categories that can be leveraged to improve the member retention rate and lower member attrition.

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CFS Loan Risk Model

The CFS Loan Risk model measures the difference between the expected and actual payment behavior to create a near real time risk assessment of each collateralized loan.

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CFS Loan Projection Model

The CFS Loan Projection models utilizes the original loan terms along with the payment transactions to project the originally expected, actual and re-forecasted cash flows for the loan. This insightful data allows for more accurately projecting the expected revenue from the loan portfolio.

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CFS Member Revenue Model

CFS Insight’s Member Revenue model generates loan and share based revenue data at a member and product level over time to inform product promotions and member interactions.

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CFS Geo Spatial Model

The CFS Geospatial model incorporates geocoding , Census, FDIC and NCUA data into an insightful and interactive dashboard that enables data driven planning for current and future ATM and branch locations.

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CFS Member Fee Model

Credit Unions are looking at fees in a variety of ways and asking such questions as Are we consistent in our handling of fees in accordance with our policy? Should we follow other financial insitituctions in eliminating or reducing fees? If we do alter our fee policy, how will this impact our membership and bottom line? This model provides data driven answers these and other important fee-related questions.

- 4-step CFS Process

The way to unlocking your credit union data

  • 1

    Evaluate & Discover
  • 2

    Create & Deploy
  • 3

    Engage & Onboard
  • 4

    Support & Review