Predicting the Purchase of Self-Employed Pension Schemes in the Iranian Social Security Organization Using Decision Tree and Random Forest Classification Algorithms

Ghasemi, Yasin; Khandan, Abbas; Akbarpour-Roshan, Narges

Volume 13, Issue 47 (5-2022) jemr 2022, 13(47): 115-165 | Back to browse issues page

‎ 20.1001.1.22286454.1401.13.47.4.4

Mendeley

Zotero

RefWorks

Ghasemi Y, Khandan A, Akbarpour-Roshan N. Predicting the Purchase of Self-Employed Pension Schemes in the Iranian Social Security Organization Using Decision Tree and Random Forest Classification Algorithms. jemr 2022; 13 (47) :115-165
URL: http://jemr.khu.ac.ir/article-1-2280-en.html

Predicting the Purchase of Self-Employed Pension Schemes in the Iranian Social Security Organization Using Decision Tree and Random Forest Classification Algorithms

Yasin Ghasemi¹

, Abbas Khandan ^*

², Narges Akbarpour-Roshan³

1- University of Kharazmi
2- Faculty of economics, University of Kharazmi , khandan.abbas@khu.ac.ir
3- Faculty of economics, Institute for Humanities and Cultural Studies

Abstract: (2636 Views)

The pension coverage of the Iranian Social Security Organization for self-employed workers is offered at three contribution rates of 12, 14 and 18 percent, but looking at the statistics shows that the demand for these types of insurances is low. This research investigates the characteristics of these insured groups by using data mining and applying two machine learning algorithms, decision tree and random forest, and predicts their behavior by providing a classification model. This will help the Social Security Organization to improve customer relationship management. For this purpose, the information of 1286174 insured persons of self-employed in 2020 was used, which includes the characteristics of age, gender, average monthly income, the years of service, and the type of self-employed pension scheme. The obtained results show that women mainly apply for the scheme with 12 percent contribution, while men tend to be covered by schemes with contribution rates of 14 and 18 percent due to the burden of supporting the family. Also, for men, the demand for schemes of 14 and 18 percent increases with the increase of age, income and years of service, but there are no such trends for women. According to the obtained results, years of service and then gender are decisive in choosing the type of pension scheme in such a way that according to the prediction of the model, people with less than 4.5 years of service are known as definite applicants for 12 percent self-employed pension scheme.

Keywords: Pension Insurance of self-employed, Social Security Organization, Data mining, Machine learning, Classification

Full-Text [PDF 812 kb] (1259 Downloads)

Type of Study: Applicable | Subject: بخش عمومی
Received: 2022/11/15 | Accepted: 2023/04/15 | Published: 2023/07/2

References

1. Abdi, A. (2006). Examining the issues and problems of insured persons and the free and optional jobs in interaction with the social security organization. Social Security Quarterly, 8(1). 255-282. (In Persian)

2. Abdi, F., Khalili-Damghani, K., & Abolmakarem, S. (2017). Solving customer insurance coverage sales plan problem using a multi-stage data mining approach. Kybernetes.1, 2-19. [DOI:10.1108/K-07-2017-0244]

3. Abdul-Rahman, S., Arifin, N. F. K., Hanafiah, M., & Mutalib, S. (2021). Customer Segmentation and Profiling for Life Insurance using K-Modes Clustering and Decision Tree Classifier. International Journal of Advanced Computer Science and Applications (IJACSA), 12(9) [DOI:10.14569/IJACSA.2021.0120950]

4. Azzone, M.; Barucci, E.; Mancayo, G.G.; Marazzina, D. (2022). A Machine Learning Model for Lapse Prediction in Life Insurance Contracts. Expert Systems with Applications, 191. [DOI:10.1016/j.eswa.2021.116261]

5. Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (2000). CRISP-DM 1.0: Step-by-step data mining guide. SPSS inc, 9,13.

6. Chen, I. J., & Popovich, K. (2003). Understanding customer relationship management (CRM): People, process and technology. Business process management journal. 672-688. [DOI:10.1108/14637150310496758]

7. Chen, Y., & Hu, L. (2005). Study on data mining application in CRM system based on insurance trade. In Proceedings of the 7th international conference on Electronic commerce. 839-841. [DOI:10.1145/1089551.1089715]

8. Hurwitz, J., & Kirsch, D. (2018). Machine learning for dummies. IBM Limited Edition, 75. [DOI:10.1201/9780429196645-6]

9. Hosseini, S. M. S., Maleki, A., & Gholamian, M. R. (2010). Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty. Expert Systems with Applications,37(7), 5259-5264. [DOI:10.1016/j.eswa.2009.12.070]

10. Kracklauer, A. H., Mills, D. Q., & Seifert, D. (2004). Customer management as the origin of collaborative customer relationship management. In Collaborative Customer Relationship Management (pp.3-6). Springer, Berlin, Heidelberg [DOI:10.1007/978-3-540-24710-4_1]

11. Khalili-Damghani, K., Abdi, F., & Abolmakarem, S. (2019). Solving customer insurance coverage recommendation problem using a two-stage clustering-classification model. International Journal of Management Science and Engineering Management, 14(1), 9-19. [DOI:10.1080/17509653.2018.1467801]

12. Kong, H.; Yun, W.; Joo, W.; Kim, J.H.; Kim, K.K.; Moon, I.C.; & Kim, W.C. (2022). Constructing a personalized recommender system for life insurance products with machine-learning techniques. Intelligent Systems in Accounting, Finance and Management, 29 (4). 242-253. [DOI:10.1002/isaf.1523]

13. Maimon, O. Z., & Rokach, L. (2014). Data mining with decision trees: theory and applications (Vol. 81). World scientific

14. Mau, S., Pletikosa, I., & Wagner, J. (2018). Forecasting the next likely purchase events of insurance customers: A case study on the value of data-rich multichannel environments. International Journal of Bank Marketing, 1123-1144. [DOI:10.1108/IJBM-11-2016-0180]

15. Mollamohammadi, R., & Mostofi, M.R. (2014). The Factors Affecting the Success of the Social Security Organization in Paying Retirement Pension to Those Insured by Qom'First Branch of Social Security Organization. Organizational Culture Management, 12(2), 299-323. (In Persian)

16. Motdin, N.; Nazarian, R.; Daman-Ksheideh, M., & Seifipour, R. (2021). Designing a Comparative Model of Bank Credit Risk Using Neural Network Models, Survival Probability Function and Support Vector Machine. Journal of Economic Modeling Research, 11 (45), 199-230. (In Persian) [DOI:10.52547/jemr.12.45.199]

17. Motafakkerazad, M.A.; & Ghafarnejad Mehraban, A. (2011). Intelligent Modeling of Asymmetric Effects of Monetary Shocks on Output in Iran(Neural Network Application). Journal of Economic Modeling Research, 2 (4), 83-102. (In Persian)

18. Najafi, A. (2019). Predictability of loyalty and separation of self-insurance Persons of Social Security Organization based on data mining method. Social Security Quarterly, 15(1). 88-109. (In Persian)

19. Ngai, E. W., Xiu, L., & Chau, D. C. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert systems with applications,36(2), 2592-2602. [DOI:10.1016/j.eswa.2008.02.021]

20. Parmah, S.; Mardomdar, S., & Heidari, A. (2020). Macroeconomic Variables and Demand for Self-employment Insurance in the Social Security Organization. Social Security Quarterly, 16(1). 41-59. (In Persian)

21. Rahman, S., Arefin, K. Z., Masud, S., Sultana, S., & Rahman, R. M. (2017, April). Analyzing Life Insurance Data with Different Classification Techniques for Customers' Behavior Analysis. In Asian Conference on Intelligent Information and Database Systems.15-25. [DOI:10.1007/978-3-319-56660-3_2]

22. Rygielski, C., Wang, J. C., & Yen, D. C. (2002). Data mining techniques for customer relationship management. Technology in society, 24(4), 483-502. [DOI:10.1016/S0160-791X(02)00038-6]

23. Severino, Matheus Kempa, and Yaohao Peng. "Machine learning algorithms for fraud prediction in property insurance: Empirical evidence using real-world microdata." Machine Learning with Applications 5 (2021): 100074. [DOI:10.1016/j.mlwa.2021.100074]

24. Shokohyar, S.; Rezaeian, A., & Boroufar, A. (2017). Identifying the customer behavior model in life insurance Sector using data mining. Management Research in Iran, 20(4). 65-94. (In Persian)

25. Sullivan, W. (2017). Machine Learning For Beginners Guide Algorithms: Supervised & Unsupervsied Learning. Decision Tree & Random Forest Introduction. Healthy Pragmatic Solutions Inc

26. Tanha, J., Van Someren, M., & Afsarmanesh, H. (2017). Semi-supervised self-training for decision tree classifiers. International Journal of Machine Learning and Cybernetics, 8(1), 355-370. [DOI:10.1007/s13042-015-0328-7]

27. Wirth, R., & Hipp, J. (2000, April). CRISP-DM: Towards a standard process model for data mining. In Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining. 29-40.