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

Author

Listed:
  • Mahdavi, Ghadir

    (ECO College of Insurance, Allameh Tabataba’i University, Tehran, Iran)

  • Heidarzadeh Azar, Ramin

    (Allameh Tabataba’i University)

  • Ofoghi, Reza

    (ECO College of Insurance, Allameh Tabataba’i University, Tehran, Iran)

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

Suggested Citation

  • Mahdavi, Ghadir & Heidarzadeh Azar, Ramin & Ofoghi, Reza, 2025. "Predicting Life Insurance Policyholder Churn in Iran Using Machine Learning: A Transparent and Actionable Framework," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 20(4), pages 581-596, December.
    • Handle: RePEc:mbr:jmonec:v:20:y:2025:i:4:p:581-596
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