Introduction

Insurance claim severity—the financial loss amount requiring indemnification—varies dramatically based on claim circumstances, insured characteristics, and external factors. Predicting severity accurately enables insurers to allocate loss reserves properly, manage catastrophic risk, and optimize reinsurance coverage. Gradient boosting models leveraging historical claim data and contextual features predict severity achieving significantly stronger predictive performance than traditional actuarial approaches.

Model Development and Feature Engineering

XGBoost and LightGBM models trained on historical claim data covering millions of claims predict severity based on claim type and circumstances, insured characteristics, incident details, and macroeconomic context. Models learn complex non-linear relationships between features and claim severity impossible to capture with linear approaches.

Results and Performance Improvements

Gradient boosting models achieve RMSE (root mean square error) reductions of 25-35% compared to traditional actuarial chain-ladder approaches, enabling significantly more accurate loss reserve estimation.

Applications and Business Impact

Severity predictions enable reserve adequacy assurance, reinsurance optimization identifying which claim types require additional reinsurance, and claims management prioritization directing investigator resources to highest-loss claims.

Conclusion

Gradient boosting improves claim severity prediction accuracy enabling better reserve management and risk optimization.