Financial institutions in a nation playa crucial role in the development of itsrneconomy. The banking sector as one type offinancial institution is indisputably thernnew ji'ontier of economic development in a country. In this respect, banking has tornbe sound and safe jar its clistomers as well as jar the stability of the currency andrneconomy of a counl1y. One factor that affects the well fimctioning of the bankingrnsector is credit risk. This factor is also a general problem among commercialrnbanks in Ethiopia.rnIn order to deal with high default rates banks in other countries are making use ofrndata mining. The possible application of data mining in the commercial bankingrnsector of Ethiopia has also been tested by the use of neural network techflique. Asrncredit risk is a risk type that bank managers give more emphasis in the loanrndisbursement process because it is one of the major reasons that cause a bank tornfail, the study of the possible application of data mining needed jilrtherrninvestigation. To this end, the present study focuses on the application of datarnmining to support credit risk assessment taking as a case study Nib InternationalrnBank S.C.(NIB). In doing so the aim of this research was to assess the potentialrnapplicability of decision tree technique to help in the loan disbursement decisionmakingrnprocess of banks.rnThe methodology used for this research had three basic steps. These wererncollecting of data, data preparation, and model building and testing. The requiredrndata was selected and extracted ji'01l/ Nib International Bank records. Then, datarnpreparation tasks (such as data tram!ormation, deriving of new fields, andrnhandling of missing variables) were undertaken. Decision tree data miningrntechnique was employed to build and test models.rn,rnSeveral decision tree models were built and testedfor their classification accuracyrnand the model with encouraging results was taken to generate rules to supportrncredit decision makers and the procedures adopted are described in this document .The peliormance of the developed model is validated using new datasets and itsrnpredictive accuracy is also tested. The result shows that the use of decision treerntechnique produces rules for justifiable credit decision-making and that it is thernbest technique that needs to be adopted for NIB bank as it presents a means ofrnproviding explanation for proposed decisions as compared to neural networkrntechniqlles.rnA 1/ things considered, the existence of an electronic system to support the creditrnrisk assessment of NIB bank will promote the services of the bank to its customersrnas well as minimize risk