Predicting Life Insurance Policyholder Churn in Iran Using Machine Learning: A Transparent and Actionable Framework

Ghadir Mahdavi, Ramin Heidarzadeh Azar and Reza Ofoghi
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Ghadir Mahdavi: ECO College of Insurance, Allameh Tabataba’i University, Tehran, Iran
Ramin Heidarzadeh Azar: Allameh Tabataba’i University
Reza Ofoghi: ECO College of Insurance, Allameh Tabataba’i University, Tehran, Iran

Journal of Money and Economy, 2025, vol. 20, issue 4, 581-596

Abstract: This study tackles the challenge of customer churn in life insurance, which leads to substantial financial losses. It introduces a transparent, reproducible, and leakage-free machine learning framework designed to identify at-risk policyholders accurately and efficiently. Using 20,000 anonymized Iranian life insurance policies with a churn rate of 26%, the study develops a complete

Keywords: Customer churn prediction; Life insurance; Machine learning; Data leakage prevention; Retention strategy (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:mbr:jmonec:v:20:y:2025:i:4:p:581-596

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