Using support vector machines for the commercial bank credit risk assessment

In this article,we introduce a different method called the overall risk measure in the bank which is different from the “enterprise-bank” internal measure from the micro system. this method measures the credit risks of an enterprise in a macro perspective. specially,we take the ratio of non-performing loan (npl) as the indicator to measure credit risks and use the support vector machines (hereinafter referred as: svm) to predict it,as the support vector machines have advantages in processing high-dimension samples. furthermore,we choose some indicators relevant with ratio of non-performing loan and use the principle component analysis and the recursive feature elimination to exclude indicators. and then support vector regression (hereinafter referred as: svr) train samples to output ratio of non-performing loan. when comparing all kinds of ways to choosing indicators,we find out a regression model combined by deleting insignificant indicators and recursive feature elimination has the better prediction accuracy than the bp neural network. finally,the empirical results evidence that svr has a good prediction. © 2014 pakistan journal of statistics.