From Risk Assessment to Risk Prediction with ML
Powering smarter underwriting with machine learning–based risk modeling
The Challenge
Our Approach
We built a machine learning-powered risk engine using gradient boosting and neural networks trained on 10+ years of historical policy, claim, and demographic data. The project included:
- Feature engineering pipelines for 200+ variables (behavioral, geographic, transactional)
- Custom risk models per product line (e.g., auto, property, health)
- Model interpretability using SHAP values for regulatory compliance
- Integration into their existing quoting system via RESTful APIs
Stats
The Outcome
- 30% improvement in underwriting accuracy
- Reduced quote turnaround time from 3 days to under 30 minutes
- Enabled dynamic pricing for niche risk segments
- Provided underwriters with explainable model-driven insights
Case Study
- All Posts
- AI Agents
- AI Chat Bot
- AI Kickstater
- Computer Vision
- Data Engineering
- Gen AI
- Machine Learning
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