Modeling Based on RS and BPNN for Credit Risk Assessment in Commercial Banks
J. Zhu · Jisuanji fangzhen · 2011
Study commercial bank credit risk assessment.Assessing credit risk in commercial banks,involves many appraisal targets,and the indexes have much redundant information,therefore,the traditional method is unable to eliminate these redundancies,and the result precision is not very high.In order to improve the credit risk assessment of commercial banks,a combined commercial bank credit risk assessment model(RS_BPNN) of rough set theory(RS) and the BP neural network(BPNN) is put forward.Firstly,rough sets theory of numerical analysis of strong capability evaluation indexes is used for attribute reduction,then the BP neural network training data are reduced to simplify the network structure.Secondly,the reduced BP neural network is trained.Finally,the simulation experiment is carried out.The results show that compared with the traditional BP neural network model,the combined model speeds up the network operating speed,further enhances assessubg precision,and has obtained the good appraisal result.