Insurance

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.

Key Metrics

28%
Loss Reduction
12%
Profit Increase
99.3%
Model Accuracy
10x
ROI

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:

  1. Ensemble prediction models combining multiple algorithms
  2. Feature engineering pipeline to extract insights from claims data
  3. External data enrichment using industry and public datasets
  4. Explainable AI components to ensure transparency in underwriting
  5. 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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