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 productst
