Using Machine Learning Algorithms to Predict the State of Financial Inclusion in Africa

The financial economy in Africa faces significant challenges that affect development and livelihood. One of these challenges is holding a bank account in Africa, indicating the person’s stable economic status. There is a need to solve bank problems in Africa and find solutions to the banking problems. Studies on this topic consider the enormous number of people who do not have a bank account compared to those who have and how this contributes to the decline of Africa’s economy. Therefore, in this research, we have implemented effective mechanisms using machine learning techniques to predict who owns a bank account and who is not in African banks. We used different machine learning algorithms, such as SVM, Naive Bays, Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Bagging, AdaBoosting, Voting Ensemble, KNN, Stack, and XGBoosting Classifiers. We have experimented with these techniques on a public dataset obtained from African banks (publically available on Zindi) to predict whether a person has a bank account or not. We used the Holdout cross-validation method to split the training dataset randomly to train and validation. The results show that the XGBoost model has a superior accuracy score of 89.23%. This paper provides a comprehensive comparison for all mentioned models, which we used to perform our study.