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A Comparative Analysis of Machine Learning Models for the Prediction of Insurance Uptake in Kenya

Author

Listed:
  • Nelson Kemboi Yego

    (African Center of Excellence in Data Science, University of Rwanda, Kigali, Rwanda
    Faculty of Sciences, Department of Mathematics and Computing, Moi University, Eldoret 3900-30100, Kenya)

  • Juma Kasozi

    (African Center of Excellence in Data Science, University of Rwanda, Kigali, Rwanda
    Faculty of Physical Sciences, Department of Mathematics, Makerere University, Kampala 7062-10218, Uganda)

  • Joseph Nkurunziza

    (African Center of Excellence in Data Science, University of Rwanda, Kigali, Rwanda
    School of Economics, University of Rwanda, Kigali, Rwanda)

Abstract

The role of insurance in financial inclusion and economic growth, in general, is immense and is increasingly being recognized. However, low uptake impedes the growth of the sector, hence the need for a model that robustly predicts insurance uptake among potential clients. This study undertook a two phase comparison of machine learning classifiers. Phase I had eight machine learning models compared for their performance in predicting the insurance uptake using 2016 Kenya FinAccessHousehold Survey data. Taking Phase I as a base in Phase II, random forest and XGBoost were compared with four deep learning classifiers using 2019 Kenya FinAccess Household Survey data. The random forest model trained on oversampled data showed the highest F1-score, accuracy, and precision. The area under the receiver operating characteristic curve was furthermore highest for random forest; hence, it could be construed as the most robust model for predicting the insurance uptake. Finally, the most important features in predicting insurance uptake as extracted from the random forest model were income, bank usage, and ability and willingness to support others. Hence, there is a need for a design and distribution of low income based products, and bancassurance could be said to be a plausible channel for the distribution of insurance products.

Suggested Citation

  • Nelson Kemboi Yego & Juma Kasozi & Joseph Nkurunziza, 2021. "A Comparative Analysis of Machine Learning Models for the Prediction of Insurance Uptake in Kenya," Data, MDPI, vol. 6(11), pages 1-17, November.
    • Handle: RePEc:gam:jdataj:v:6:y:2021:i:11:p:116-:d:679105
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    References listed on IDEAS

    as
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