Risk-Selection Models for Claims
Challenge
An insurance provider needed to identify low-propensity high-cost claims to optimize underwriting processes and reduce losses.
Solution
We developed sophisticated risk-selection models using machine learning to predict potential high-cost claims during the underwriting process, enabling more accurate pricing and risk assessment.
Results
The models substantially reduced overall loss ratio and increased profitability. The solution was integrated seamlessly with existing underwriting systems and provided transparent explanations for decisions.
Project Overview
This project helps a major insurance provider identify potentially costly claims early in the underwriting process, enabling better pricing and risk management.
Technical Solution
Model Approach
We developed a sophisticated risk modeling solution including:
- Ensemble prediction models combining multiple algorithms
- Feature engineering pipeline to extract insights from claims data
- External data enrichment using industry and public datasets
- Explainable AI components to ensure transparency in underwriting
- Automated monitoring to detect model drift and performance issues
Technical Implementation
The solution integrates several advanced techniques:
- Gradient boosted decision trees for primary prediction
- Neural networks for complex pattern recognition
- Bayesian models for uncertainty quantification
- SHAP values for model explainability
- Automated retraining pipeline with champion/challenger testing
Implementation Challenges
Key challenges we addressed included:
- Working with highly imbalanced datasets for rare claim events
- Meeting strict regulatory requirements for model transparency
- Integrating with legacy underwriting systems
- Creating intuitive interfaces for non-technical underwriters
- Handling missing data in historical claims records
Business Impact
The system delivered substantial benefits:
- Significant reduction in loss ratio on targeted policies
- Overall profit increase across the portfolio
- Better pricing competitiveness in lower-risk segments
- More accurate reserving for potential claims
- Increased underwriter productivity through automation
Technology Stack
- Python with scikit-learn and XGBoost
- R for statistical validation
- SQL Server for data storage
- Docker containers for deployment
- PowerBI for underwriter dashboards
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