Issue

Vol 39 No 2 (2024): May

Date Log

Submitted
Aug 3, 2023
Accepted
Jan 26, 2024
Published
May 8, 2024

Risk-Based Premiums of Insurance Guarantee Schemes: A Machine-Learning Approach

https://doi.org/10.22146/jieb.v39i2.9323
Citra Amanda
Finance Program, School of Accounting, Universitas Bina Nusantara, DKI Jakarta, 11480, Indonesia
Ananta Dian Pradipta
Group of Transformation Management, Indonesia Deposit Insurance Corporation, DKI Jakarta, 12190, Indonesia

Journal of Indonesian Economy and Business, Vol 39 No 2 (2024): May

Abstract

Introduction/Main Objectives: This study explores the application of machine-learning techniques to risk-based premium calculations for insurance guarantee schemes within the Indonesian insurance market. This study aims to develop a risk-based premium calculation model using machine-learning techniques in the Indonesian context. Background Problems: A gap exists in determining risk-based premiums for both the life and non-life insurance sectors within the Indonesian insurance market. Identifying and understanding the key variables that significantly influence risk-based capital (RBC) is important, and this research addresses this need. Novelty: This paper is the first to apply machine learning to calculate risk-based premiums in the context of the Indonesian insurance market. The distinction between the life and non-life insurance sectors in terms of the importance of its variables and itsselection of an optimal model further enrich its unique approach. Research Methods: We employed gradient-boosted and decision-tree models to identify key factors impacting risk-based capital. Furthermore, we leveraged clustering techniques to categorize companies into distinct risk tiers, aiming to enable more precise risk-based premium rate calculations. Finding/Results: The findings reveal significant differences between the life and non-life insurance sectors in terms of key variables that impact their risk-based capital. These insights lead to the categorization of insurance companies into distinct risk tiers whichhelps to more accurately calculate risk-based premiums. Conclusion: Machine learning can serve as a powerful tool in refining insurance risk management practices, ultimately benefiting insurers, policyholders, and regulators alike.

Keywords

risk-based capital insurance guarantee schemes machine learning Indonesian insurance market clustering techniques
Amanda, C., & Pradipta, A. D. (2024). Risk-Based Premiums of Insurance Guarantee Schemes: A Machine-Learning Approach. Journal of Indonesian Economy and Business, 39(2), 121–142. https://doi.org/10.22146/jieb.v39i2.9323
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