Developing A Case Based Credit Approval System Using Data Mining: The Case Of Commercial Bank Of Ethiopia

Abstract

The very nature of the banking business is so sensitive because more than 85% of their liability is deposits from customers. Banks use these deposits to generate credit for their borrowers, which in fact is a revenue generating activity for most banks. However, this credit creation process exposes Banks to high default risk, which might lead to financial distress including bankruptcy. So banks need to manage credit risk inherent in the entire portfolio as well as the risk in individual credits or transactions. In this study therefore, a case-based credit approval decision making knowledge base system that uses data mining results is proposed by applying empirical research design. The researcher used manual and automated knowledge acquisition techniques, such as interview, document analysis and data mining. To identify the best prediction model for Credit approval decision making, three experiments using three classification algorithms were conducted. Finally, the researcher decided to use the results of J48 decision tree classification algorithm in the development of the prototype case-base System because it registered better performance than other classifiers. The developed model was tested with test instances and only those instances registers more than 95% accuracy were used to develop a knowledge base for the CBR development for a better efficiency. Then, the implementation of the prototype using JCOLIBERI version 1.1 which is object oriented case-based reasoning framework is realized. Finally, testing of the prototype case-based reasoning system is done to evaluate the performance of the system. The prototype is evaluated using system testing and user acceptance testing. Testing system performance in terms of precision, recall and f-measure registered 83%, 73 % and 77 %, respectively. Also user acceptance testing achieved 83.2% performance. The evaluation of the prototype shows a promising result to design an applicable intelligent system that supports effective and efficient credit approval decisions making. But, the current system suggests no explanation about the correct action to be taken; as a result a hybrid explanation driven system by combining Case based reasoning with Rule based reasoning is recommended as a future research direction.