Solutions /  Use cases /  Insurance / Fraudulent Claim Modeling

Fraudulent Claim Modeling

DataRobot predicts which insurance claims are most likely fraudulent based on claim characteristics.

Problem / Pain

Fraudulent claims are costly, but it is too expensive and inefficient to investigate every claim. Even if possible, investigating innocent customers could prove to be a very poor experience for the insured, leading some to leave the business.

Solution

Using DataRobot, you can automatically build extremely accurate predictive models to identify and prioritize likely fraudulent activity. Fraud units can then create a data-based queue, investigating only those incidents likely to require it. The resulting benefits are two-fold. First, your resources are deployed where you will see the greatest return on your investigative investment. Additionally, you optimize customer satisfaction by not challenging innocent claims.

Why DataRobot

Automate building an accurate model to predict the likelihood that a claim is fraudulent. With the results, you can create a rank-ordered queue of claims for fraud units to investigate.

Want to see the DataRobot automated machine learning platform in action?