Credit risk analysis in Sudanese commercial banking sector using Data mining techniques

Abstract

In this research, financing data was used from a commercial bank in Sudan (2010-2013), this data contains 17280 records and 60 attributes. Aimed to discover rules and build a model to assist in the financing decision and tested the degree of accuracy for this model. Data have been prepared resulted 8 attributes before mining, then used WEKA software for Data Mining, which got 4 attributes after attribute selection to get related data. used classification and built the model using test option (cross validation) the degree of accuracy of the model 94.06 and used test option ( percentage split) and accuracy was 94.18 and created a decision tree that had size of 11 nodes. The result was a 9 rules and the decision model that