Combining Support Vector Machines for Credit Scoring

Abstract

Support vector machines (SVM) from statistical learning theory are powerful classification methods with a wide range of applications including credit scoring. The urgent need to further boost classification performance in many applications leads the machine learning community into developing SVM with multiple kernels and many other combined approaches. Owing to the huge size of the credit market, even small improvements in classification accuracy might considerably reduce effective misclassification costs experienced by banks. Under certain conditions, the combination of different models may reduce or at least stabilize the risk of misclassification. We report on combining several SVM with different kernel functions and variable credit client data sets. We present classification results produced by various combination strategies and we compare them to the results obtained earlier with more traditional single SVM credit scoring models.