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Ptimizing Life Insurance Portfolio Management in Egypt through Al-Powered Prediction of Policy Lapses and Cancellations
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Ptimizing Life Insurance Portfolio Management in Egypt through Al-Powered Prediction of Policy Lapses and Cancellations
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Journal Article
Ptimizing Life Insurance Portfolio Management in Egypt through Al-Powered Prediction of Policy Lapses and Cancellations
2025
Overview
Policy lapses and cancellations pose significant challenges to the stability and profitability of the Egyptian insurance market. This study evaluates the effectiveness of machine learning (ML) models - Logistic Regression, Random Forest, Support Vector Machines (SVM), and Artificial Neural Networks (ANN) - to predict lapses based on policyholder data and behavioral trends. Key predictors such as income, age, premium amount, payment frequency, and policy type were analyzed to identify actionable insights. The findings highlight the transformative potential of Al in improving customer retention, optimizing operational efficiency, and addressing critical market challenges.
Publisher
جامعة بورسعيد - كلية التجارة
