Enhanced insurance analytics: Precision through data and statistics
Keywords:
Poisson model, decision tree, claims, automobile insuranceAbstract
This study investigates the factors influencing claims frequency and costs in the Algerian automobile insurance market, a sector currently stagnating due to reduced vehicle imports. Using comprehensive datasets from the Algerian Insurance Company (SAA), a dual-methodological approach is employed: the Poisson model is applied to analyze claims frequency, while decision tree techniques are utilized to identify key variables shaping claims behavior. The research primarily seeks to uncover the critical determinants of reported accidents and associated claims costs, addressing the central question: “What are the most significant variables influencing claims frequency and costs?” By doing so, the study aims to offer valuable insights into effective risk classification and premium adjustment strategies. The findings are anticipated to enhance understanding of claims dynamics within the Algerian insurance sector, providing actionable recommendations for insurers to improve operational efficiency and refine risk management practices. While tailored to the Algerian context, this research also holds relevance for other markets confronting similar challenges in automobile insurance.
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WWW.SAA.dz
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Copyright (c) 2024 Abdelaziz Sahraoui, Roufaida Souakri

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