Abstract

Credit risk is one of the three components making up financial risk. Under the New Basel Capital Accord,default risk has been listed as the most important factor for credit risk among all elements that affect risk ofcredit. Banks in China currently leave large quantities of cash idle due to difficulty in loan recovery. This essayfirst analyzes the distributional features of variables’ cross-section data concerning the default rate. Based oncredible data, this research then undertakes the choice of an appropriate default prediction model. The BinaryLogistic Regression Model is adopted here to build the default rate model of business credit risk and to analyzethe risk information generated, in hopes of helping banks find the correct loaning strategies.

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