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Insurance Analytics Case study

The insurance sector is becoming more competitive and new operating models are coming into the spotlight. In a dynamic business environment, insurance payers must sustain their efforts in marketing and enrolment, building customer retention, and striking a balance between their profitability, purpose and perceived value.

When it comes to insurance claims processing, the challenges are multi-faceted – volume, accuracy, speed of processing, administrative costs and regulatory compliance all have a hand in improving customer satisfaction.

How We Made Difference?

One of leading insurance claim processing company with a very large insurance claims processing requirements with a significant manual review process.

An automotive insurance company needed to identify potentially fraudulent personal injury claims early in the claims process to avoid costly payouts. Manually examining every claim for potential fraud is impractical for most insurance companies given the high volume of claims and the extensive time/resources a manual review requires. The key to an effective insurance fraud solution is to identify both high and very-low risk claims early in the claims process.

The client chose a claims fraud solution built with R & python that incorporated business rules and predictive models to identify high and low risk claims. Predictive models were also trained to identify the propensity for each claim to be fraudulent.

The client was able to identify high risk claims within the existing claims base. Examiner verification of the presence of fraud on the high risk claims showed a very high hit rate (>90%) of fraudulent claims.