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.
Highlights
In order to fast track the development of rural areas in Ghana, rural banks were introduced. e Association of Rural Banks (ARB) described some of the roles of rural banks as follows: cultivating the habits of savings among rural inhabitants, mobilizing resources locked up in the rural areas into the banking systems to facilitate development, and identifying viable industries in their respective areas for investment and development [1]
It is believed that financial institutions are powerful tools to mitigate poverty [2], but some of them are rather increasing the burden of people through illegal activities and mismanagement of resources [3]. e collapse of most of these institutions can be seen as a result of them not being able to evaluate and predict their financial standings in the years ahead [4]. erefore, the main objective of this project is to develop and propose a predictive model capable of predicting the financial standing of financial institutions as well as identifying the most influencing financial ratios, using rural banks in Ghana as a case study area and decision tree algorithms (C5.0, C4.5, and Classification and Regression Tree (CART))
Machine learning algorithms are making things easier as they learn from precedence and forecast future events. ere are numerous machine learning algorithms, but the one which is very easy to savvy by most people, mathematically inclined people, is the decision tree due to its easy-to-understand graphical representation. is study used DT algorithms in building a model that can forecast the financial status of a financial institution
Summary
In order to fast track the development of rural areas in Ghana, rural banks were introduced. e Association of Rural Banks (ARB) described some of the roles of rural banks as follows: cultivating the habits of savings among rural inhabitants, mobilizing resources locked up in the rural areas into the banking systems to facilitate development, and identifying viable industries in their respective areas for investment and development [1]. In order to fast track the development of rural areas in Ghana, rural banks were introduced. It is believed that financial institutions are powerful tools to mitigate poverty [2], but some of them are rather increasing the burden of people through illegal activities and mismanagement of resources [3]. E collapse of most of these institutions can be seen as a result of them not being able to evaluate and predict their financial standings in the years ahead [4]. Erefore, the main objective of this project is to develop and propose a predictive model capable of predicting the financial standing of financial institutions as well as identifying the most influencing financial ratios, using rural banks in Ghana as a case study area and decision tree algorithms (C5.0, C4.5, and CART). E remainder of this paper is organized as follows: the section (Section 2) provides a literature review; Section 3 presents the methodology developed and followed in this study and documents its findings; Section 4 summarizes and concludes the paper
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
