Drivers of potential policyholders’ uptake of insurance in Kenya using Random Forest
-
DOIhttp://dx.doi.org/10.21511/ins.14(1).2023.03
-
Article InfoVolume 14 2023, Issue #1, pp. 22-34
- 380 Views
-
222 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
The low adoption of insurance by potential policyholders in developing countries like Kenya is a cause for concern for insurers, regulators, and other marketing stakeholders. To effectively design targeted marketing strategies to boost insurance adoption, it is crucial to determine the factors that affect insurance uptake among potential policyholders. In this study, the 2021 FinAccess Survey, which interviewed sampled individuals above 16 years in Kenya and machine learning techniques, including Random Forest, XGBoost, and Logistic Regression, were utilized to uncover the factors driving insurance uptake and the reasons for the low adoption of insurance among potential policyholders. Random Forest was the most robust model of the three classifiers based on Kappa score, recall score, F1 score, precision, and area under the operating characteristic curve (approaching 1). The paper explores eight reasons why people currently do not have insurance policies. The results indicated that affordability was the primary driver of uptake with 68.67% of having expressed a desire to possess insurance but are unable to afford it. The highest level of education being the next most significant factor. Cultural and religious beliefs and mistrust of insurance providers were found to have a minimal impact on uptake. These findings imply that offering affordable insurance products and conducting awareness campaigns are critical to increase insurance adoption.
- Keywords
-
JEL Classification (Paper profile tab)G22, G52, C38, D14
-
References40
-
Tables5
-
Figures2
-
- Figure 1. Areas under the ROC curve (AUC)
- Figure 2. Feature importance
-
- Table 1. Reasons why people currently do not have an insurance policy
- Table 2. Model metrics on imbalanced data
- Table 3. Model metrics on SMOTE balanced data
- Table 4. Model metrics on oversampled data
- Table A1. Feature importance
-
- Ali, A., & Tausif, M. R. (2018). Service quality, customers’ satisfaction, and profitability: an empirical study of Saudi Arabian insurance sector. Investment Management and Financial Innovations, 15(2), 232-247.
- Ampomah, E. K., Qin, Z., & Nyame, G. (2020). Evaluation of Tree-Based Ensemble Machine Learning Models in Predicting Stock Price Direction of Movement. Information, 11(6), 332.
- Ankrah, D. A., Kwapong, N. A., Eghan, D., Adarkwah, F., & Boateng-Gyambiby, D. (2021). Agricultural insurance access and acceptability: examining the case of smallholder farmers in Ghana. Agriculture & Food Security, 10(1), 19.
- Barnes, J., O’Hanlon, B., Feeley III, F., McKeon, K., Gitonga, N., & Decker, C. (2010). Private health sector assessment in Kenya (Working Paper No. 193). World Bank Publications.
- Basak, S., Kar, S., Saha, S., Khaidem, L., & Dey, S. R. (2019). Predicting the direction of stock market prices using tree-based classifiers. The North American Journal of Economics and Finance, 47, 552-567.
- Blier-Wong, C., Cossette, H., Lamontagne, L., & Marceau, E. (2021). Machine Learning in P & C Insurance: A Review for Pricing and Reserving. Risks, 9(1), 4.
- Diana, A., Griffin, J. E., Oberoi, J. S., & Yao, J. (2019). Machine-Learning Methods for Insurance Applications-A Survey. Society of Actuaries.
- Ding, K., Lev, B., Peng, X., Sun, T., & Vasarhelyi, M. A. (2020). Machine learning improves accounting estimates: evidence from insurance payments. Review of Accounting Studies, 25(3), 1098-1134.
- Dragotă, I.-M., Cepoi, C. O., & Ştefan, L. (2022). Threshold effect for the life insurance industry: evidence from OECD countries. The Geneva Papers on Risk and Insurance - Issues and Practice.
- Guelman, L., Guillén, M., & Pérez-Marín, A. M. (2012). Random Forests for Uplift Modeling: An Insurance Customer Retention Case. Lecture Notes in Business Information Processing, 115, 123-133.
- Guo, Y., Zhou, Y., Hu, X., & Cheng, W. (2019). Research on Recommendation of Insurance Products Based on Random Forest. 2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI) (pp. 308-311). Taiyuan, China.
- Haamukwanza, C. L. (2021). To insure or not to insure – the role that government and insurance practice should play: a thematic comparison of the urban poor and the workers in the pensions and insurance industry. SN Business & Economics, 1(9), 114.
- Hanafy, M., & Ming, R. (2021). Machine Learning Approaches for Auto Insurance Big Data. Risks, 9(2), 42.
- Hou, Q., Liu, Y., Liu, J., & Sun, S. (2020). Epilepsy Detection Using Random Forest Classification Based on Locally Linear Embedding Algorithm. 2020 5th International Conference on Control, Robotics and Cybernetics (CRC) (pp. 202-206).
- Iacobucci, D., Petrescu, M., Krishen, A., & Bendixen, M. (2019). The state of marketing analytics in research and practice. Journal of Marketing Analytics, 7(3), 152-181.
- Kassambara, A. (2018). Machine learning essentials: Practical guide in R. CreateSpace Independent Publishing Platform.
- Kenya National Bureau of Statistics. (2021). FinAccess Household Survey 2021.
- Kipkogei, F., Kabano, I. H., Murorunkwere, B. F., & Joseph, N. (2021). Business success prediction in Rwanda: a comparison of tree-based models and logistic regression classifiers. SN Business & Economics, 1(8), 101.
- Kumar, A. N., Girish, S., & Suresha, B. (2023). Switching intention and switching behavior of adults in the non-life insurance sector: Mediating role of brand love. Insurance Markets and Companies, 14(1), 1-7.
- Landry, C. E., Turner, D., & Petrolia, D. (2021). Flood Insurance Market Penetration and Expectations of Disaster Assistance. Environmental and Resource Economics, 79(2), 357-386.
- Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., & Liu, H. (2017). Feature selection: A data perspective. ACM Computing Surveys (CSUR), 50(6), 1-45.
- Lin, W., Wu, Z., Lin, L., Wen, A., & Li, J. (2017). An ensemble random forest algorithm for insurance big data analysis. IEEE Access, 5, 16568-16575.
- Liu, Y., Zhang, Y., Chen, X., & Yang, Y. (2021). Superstition and farmers’ life insurance spending. Economics Letters, 206, 109975.
- Mutembei, J. M. (2022). Impact of Employees Capability Affecting the Growth of Life Insurance Business. A Critical Literature Review. Journal of Actuarial Research, 1(1), 1-12.
- Mwongela, J. N. (2022). The Influence of Regulatory Framework on Insurance Penetration in Kenya. A Case Study of the Registered Insurance Companies in Nairobi County (Master’s Thesis). Kenya Methodist University.
- Polinkevych, O., Glonti, V., Baranova, V., Levchenko, V., & Yermoshenko, A. (2022). Change of business models of Ukrainian insurance companies in the conditions of COVID-19. Insurance Markets and Companies, 12(1), 83-98.
- Porrini, D. (2017). Regulating Big Data effects in the European insurance market. Insurance Markets and Companies, 8(1), 6-15.
- Prymostka, O. (2018). Life insurance companies marketing strategy in the digital world. Insurance Markets and Companies, 9(1), 70-78.
- Quan, Z., Wang, Z., Gan, G., & Valdez, E. A. (2023). On hybrid tree-based methods for short-term insurance claims. Probability in the Engineering and Informational Sciences, 37(2), 597-620.
- Rawat, S., Rawat, A., Kumar, D., & Sabitha, A. S. (2021). Application of machine learning and data visualization techniques for decision support in the insurance sector. International Journal of Information Management Data Insights, 1(2), 100012.
- Ren, L., Seklouli, A. S., Zhang, H., Wang, T., & Bouras, A. (2023). An adaptive Laplacian weight random forest imputation for imbalance and mixed-type data. Information Systems, 111, 102122.
- Rumson, A. G., & Hallett, S. H. (2019). Innovations in the use of data facilitating insurance as a resilience mechanism for coastal flood risk. Science of The Total Environment, 661, 598-612.
- Salameh, H. (2022). An Evaluation of the financial soundness of insurance firms in the Amman Stock Exchange. Insurance Markets and Companies, 13(1), 11-20.
- Salmi, M., & Atif, D. (2022). Using a Data Mining Approach to Detect Automobile Insurance Fraud. Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021) (pp. 55-66). Springer International Publishing.
- Shehadeh, M., Kokes, R., & Hu, G. (2016). Variable Selection Using Parallel Random Forest for Mortality Prediction in Highly Imbalanced Data (pp. 13-16). Society of Actuaries.
- Sibiko, K. W., & Qaim, M. (2020). Weather index insurance, agricultural input use, and crop productivity in Kenya. Food Security, 12(1), 151-167.
- Tessema, Y. A., Hobbs, A., & Jensen, N. (2021). The Role of Learning Styles in the Uptake of Index Insurance: Evidence from Kenya (Master’s Theses). University of San Francisco.
- Wu, K., Wu, E., DAndrea, M., Chitale, N., Lim, M., Dabrowski, M., Kantor, K., Rangi, H., Liu, R., Garmhausen, M., Pal, N., Harbron, C., Rizzo, S., Copping, R., & Zou, J. (2022). Machine Learning Prediction of Clinical Trial Operational Efficiency. The AAPS Journal, 24(3), 57.
- Yadav, M. S., & Pavlou, P. A. (2020). Technology-enabled interactions in digital environments: a conceptual foundation for current and future research. Journal of the Academy of Marketing Science, 48(1), 132-136.
- Zimmermann, R., & Auinger, A. (2022). Developing a conversion rate optimization framework for digital retailers – case study. Journal of Marketing Analytics, 11, 233-243.