Using DEA for Classification in Credit Scoring

Credit scoring is a kind of binary classification problem that contains important information for manager to make a decision in particularly in banking authorities. Obtained scores provide a practical credit decision for a loan officer to classify clients to reject or accept for payment loan. For this sake, in this paper a data envelopment analysis discriminant analysis (DEADA) approach is used for reclassifying  client  to reject or accept class for case of real data sets of  an Iranian bank branch.  For this reason, two DEA models are solved. Also, the reject and accept frontiers and overlapping region among two frontiers are obtained.  Then a goal programming problem is solved for finding coefficients of the discriminant hyperplane. The results are obtained from the samples are kept from the main dataset, clarify that the classified hyper-plane obtained from the used method provides an almost profitable classification for payment loan.