Strategic Reinsurance and Explainable AI
DOI:
https://doi.org/10.58886/jfi.v23i2.10099Keywords:
Risk Management, Insurance, Machine Learning, Artificial Intelligence, Explainable AI, Property and Casualty InsuranceAbstract
This study explores the strategic determinants that impact reinsurance purchase decisions in the P&C insurance industry using the Shapley Additive exPlanations explainable artificial-intelligence (XAI) framework, or SHAP library. Key determinants such as financial considerations, competition, and industry demand for reinsurance are considered to identify their impact on different levels of ceding. The XAI process ranks these determinants based on their influence on reinsurance purchases, and identifies clear relationships between these determinants and ceding levels. For instance, an increase in writing a specific product type can lead to a lower incentive to hedge more within that product type. Additionally, this methodology also reveals more complex relationships between determinants and reinsurance purchases based on their values. Finally, the study includes a machine learning significance test for each determinant impacting insurance purchases.
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