Drivers of potential policyholders’ uptake of insurance in Kenya using Random Forest
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DOIhttp://dx.doi.org/10.21511/ins.14(1).2023.03
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Article InfoVolume 14 2023, Issue #1, pp. 22-34
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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
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JEL Classification (Paper profile tab)G22, G52, C38, D14
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References40
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Tables5
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Figures2
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- Figure 1. Areas under the ROC curve (AUC)
- Figure 2. Feature importance
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- 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
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