Predicting the Performance of Rural Banks in Ghana Using Machine Learning Approach

Abstract

The idea of rural banks was introduced as a result of limited commercial bank branches in rural areas to mobilize their resources for rural development. It is also believed that financial institutions such as rural banks are powerful tools for mitigating poverty. Nevertheless, some of these banks are rather increasing the burden of people through illegal activities and mismanagement of resources. Assessing banks’ performance using a set of financial ratios has been an interesting and challenging problem for many researchers and practitioners. Identification of factors that can accurately predict a firm’s performance is of great interest to any decision-maker. The study used ARB’s financial ratios as its independent variables to assess the performance of rural banks and later used random forest algorithm to identify the variables with the most relevance to the model. A dataset was obtained from the various banks. This study used three decision tree algorithms, namely, C5.0, C4.5, and CART, to build the various decision tree predictive models. The result of the study suggested that the C5.0 algorithm gave an accuracy of 100%, followed by the CART algorithm with an accuracy of 84.6% and, finally, the C4.5 algorithm with an accuracy of 83.34 on average. The study, therefore, recommended the usage of the C5.0 predictive model in predicting the financial performance of rural banks in Ghana.