Predicting the Performance of Rural Banks in Ghana Using Machine Learning Approach
المؤلفون المشاركون
Awoin, Emmanuel
Appiahene, Peter
Gyasi, Frank
Sabtiwu, Abdulai
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-7، 7ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-02-19
دولة النشر
مصر
عدد الصفحات
7
التخصصات الرئيسية
الملخص EN
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.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Awoin, Emmanuel& Appiahene, Peter& Gyasi, Frank& Sabtiwu, Abdulai. 2020. Predicting the Performance of Rural Banks in Ghana Using Machine Learning Approach. Advances in Fuzzy Systems،Vol. 2020, no. 2020, pp.1-7.
https://search.emarefa.net/detail/BIM-1126384
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Awoin, Emmanuel…[et al.]. Predicting the Performance of Rural Banks in Ghana Using Machine Learning Approach. Advances in Fuzzy Systems No. 2020 (2020), pp.1-7.
https://search.emarefa.net/detail/BIM-1126384
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Awoin, Emmanuel& Appiahene, Peter& Gyasi, Frank& Sabtiwu, Abdulai. Predicting the Performance of Rural Banks in Ghana Using Machine Learning Approach. Advances in Fuzzy Systems. 2020. Vol. 2020, no. 2020, pp.1-7.
https://search.emarefa.net/detail/BIM-1126384
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1126384
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر