Group Risk Scoring of Patients2020-11-10T09:47:58+00:00

15% reduction in premium using ML-backed underwriting system

Built an ML-backed underwriting system to minimize the cost of claims and assign an overall group risk score using lab/diagnostic, prescription, and claims data at multiple levels and data bridges

Business Challenges

The existing system was based on manual rules called expert systems, and lacked any predictive model.

Sigmoid’s Solution

Bridged all the datasets to extend the de-identified patients’ information to cover a multitude of features across claims, lab and prescription data.

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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