Health Insurance Premium Pricing Using Machine Learning Methods

Authors

  • Ahmad Nur Azam Ahmad Ridzuan Department of Actuarial Science, College of Computing, Informatic and Mathematics, UiTM Perak Branch Tapah Campus, 34500 Tapah, Perak
  • Aina Zafirah Azman Department of Actuarial Science, College of Computing, Informatic and Mathematics, UiTM Perak Branch Tapah Campus, 34500 Tapah, Perak
  • Fatin Alya Marzuki Department of Actuarial Science, College of Computing, Informatic and Mathematics, UiTM Perak Branch Tapah Campus, 34500 Tapah, Perak
  • Wan Shazmien Danieal Mohamed Faudzi Department of Actuarial Science, College of Computing, Informatic and Mathematics, UiTM Perak Branch Tapah Campus, 34500 Tapah, Perak
  • Siti Hajar Abd Aziz Department of Media Studies, Faculty of Mass Communication, UiTM Melaka Branch Alor Gajah Campus, 78000 Alor Gajah, Melaka
  • Norida Abu Bakar Department of Business Studies, Faculty of Business and Management, UiTM Melaka Branch Alor Gajah Campus, 78000 Alor Gajah, Melaka

DOI:

https://doi.org/10.37934/araset.41.1.134141

Keywords:

Health Insurance, Decision Tree, Neural Network, Healthcare, Premium Pricing

Abstract

Health insurance is important alongside life insurance products. This product’s subscription is gradually rising and has become one of the public’s main considerations due to their awareness on medical and surgical costs. This study is aimed at (i) identifying whether the independent factors are important in predicting the dependent variable, and (ii) to determine which model (logistic regression model/decision trees/neural networks) is the best model to be utilised. The SAS Enterprise Miner (E-Miner) was used to analyse the data and to select the best model. At the end of this study, the measurements like the Maximum Absolute Error (MAE), Average Squared Error (ASE), Root Average Squared Error (RASE), and Sum Squared Error (SSE) in the decision tree model indicated the lowest errors and led to the selection of the decision tree as the best model to be used in health insurance premium pricing.

Author Biographies

Ahmad Nur Azam Ahmad Ridzuan, Department of Actuarial Science, College of Computing, Informatic and Mathematics, UiTM Perak Branch Tapah Campus, 34500 Tapah, Perak

ahmad558@uitm.edu.my

Aina Zafirah Azman, Department of Actuarial Science, College of Computing, Informatic and Mathematics, UiTM Perak Branch Tapah Campus, 34500 Tapah, Perak

ainazafirah18@gmail.com

Fatin Alya Marzuki, Department of Actuarial Science, College of Computing, Informatic and Mathematics, UiTM Perak Branch Tapah Campus, 34500 Tapah, Perak

fatinnalyaa@gmail.com

Wan Shazmien Danieal Mohamed Faudzi, Department of Actuarial Science, College of Computing, Informatic and Mathematics, UiTM Perak Branch Tapah Campus, 34500 Tapah, Perak

2020895438@student.uitm.edu.my

Siti Hajar Abd Aziz, Department of Media Studies, Faculty of Mass Communication, UiTM Melaka Branch Alor Gajah Campus, 78000 Alor Gajah, Melaka

Shajar_aziz@uitm.edu.my

Norida Abu Bakar, Department of Business Studies, Faculty of Business and Management, UiTM Melaka Branch Alor Gajah Campus, 78000 Alor Gajah, Melaka

Norida107@uitm.edu.my

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Published

2024-03-04

How to Cite

Ahmad Ridzuan, A. N. A., Azman, A. Z., Marzuki, F. A., Mohamed Faudzi, W. S. D., Abd Aziz, S. H., & Abu Bakar, N. (2024). Health Insurance Premium Pricing Using Machine Learning Methods. Journal of Advanced Research in Applied Sciences and Engineering Technology, 41(1), 134–141. https://doi.org/10.37934/araset.41.1.134141

Issue

Vol. 41 No. 1: November (2024)

Section

Articles