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 (DEA-DA) 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 co-efficients of the discriminant hyper-plane. 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. پرونده مقاله

Conflict intermediate measures in DEA models, especially in constraint and open the black box, is the main difference between traditional DEA and network DEA models. Furthermore, from the application's perspective, intermediate measures aren’t deterministic. So, for measuring the efficiency more precisely, they can be considered as imprecise data. The aim of this paper is introducing a stochastic relational model for measuring overall efficiency that deals with intermediate and outputs as stochastic data. The proposed model is applied for portfolio optimization. An actual data set of 27 Iranian stock industries is applied as numerical example. The result shows that SR-NDEA has better discriminant power than R-NDEA model. پرونده مقاله