Smart Underwrite
ML-based insurance risk assessment and decision support tool
Summary
A machine learning-based underwriting tool trained on historical policy and claims data using scikit-learn, analyzing over 40 variables to generate risk scores.
Problem
The underwriting process in insurance companies relied heavily on manual review by experienced specialists. The same risk profile could be evaluated differently by different underwriters.
Solution
We developed a risk scoring model trained on historical policy and claims data using scikit-learn. Model decisions are supported by explainable AI (SHAP values).
Result
Tested on 10,000 historical applications in a simulation environment. Assessment time dropped from an average of 4 business days to 1.5 days. The model's risk classification accuracy was measured at 89%.
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