Group Risk Scoring System – ML Models for Premium Reduction2021-08-02T08:12:11+00:00

15% reduction in premium using ML-backed underwriting system

Built an ML-backed underwriting system for premium reduction and assign an overall group risk score using lab/diagnostic, prescription, and claims data at multiple levels and data bridges

Business Challenges

The client is a leading healthcare AI company catering to a multitude of health insurance providers and pharma companies. They were looking for a data and ML-backed underwriting system to minimize the cost of claims and offer a competitive premium while ensuring faster turn-around time for the claim processing. The existing system was based on manual rules called expert systems and lacked any predictive model.

Sigmoid’s Solution

Sigmoid bridged all the datasets to extend the de-identified patients’ information to cover a multitude of features across claims, lab and prescription data. We built machine learning models to generate a group risk score based on individual patient statuses for each employee group. We also provided recommendations regarding bidding costs, improving existing pricing and opt-out deals while creating a framework for automated report generation in turn ensuring faster turnaround time of claim processing and reduced risk of uncertainty.

Business Impact

With 50% improvement in turnaround time, our ML-backed underwriting system led to 15% reduction in cost of premium and 8% increase in margins.

For detailed understanding and solution, please download the case study here

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