Abate Yohannes (Professor)Tadesse Nigussie (PhD)Shawul Meretework2020-06-122023-11-182020-06-122023-11-182004-07http://etd.aau.edu.et/handle/12345678/21530Financial institutions in a nation playa crucial role in the development of its economy. The banking sector as one type of financial institution is indisputably the new frontier of economic development in a country. In this respect, banking has to be sound and safe jar its customers as well as jar the stability of the currency and economy of a counl1y. One factor that affects the well functioning of the banking sector is credit risk. This factor is also a general problem among commercial banks in Ethiopia. In order to deal with high default rates banks in other countries are making use of data mining. The possible application of data mining in the commercial banking sector of Ethiopia has also been tested by the use of neural network technique As credit risk is a risk type that bank managers give more emphasis in the loan disbursement process because it is one of the major reasons that cause a bank to fail, the study of the possible application of data mining needed further investigation. To this end, the present study focuses on the application of data mining to support credit risk assessment taking as a case study Nib International Bank S.C.(NIB). In doing so the aim of this research was to assess the potential applicability of decision tree technique to help in the loan disbursement decision making process of banks The methodology used for this research had three basic steps. These were collecting of data, data preparation, and model building and testing. The required data was selected and extracted ji'01l/ Nib International Bank records. Then, data preparation tasks (such as data tram formation, deriving of new fields, and handling of missing variables) were undertaken. Decision tree data mining technique was employed to build and test models. Several decision tree models were built and tested for their classification accuracy and the model with encouraging results was taken to generate rules to support credit decision makers and the procedures adopted are described in this document . The performance of the developed model is validated using new datasets and its predictive accuracy is also tested. The result shows that the use of decision tree technique produces rules for justifiable credit decision-making and that it is the best technique that needs to be adopted for NIB bank as it presents a means of providing explanation for proposed decisions as compared to neural network techniques. All things considered, the existence of an electronic system to support the credit risk assessment of NIB bank will promote the services of the bank to its customers as well as minimize risk.enInformation ScienceThesis