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