A Modified Support Vector Machine model for Credit Scoring

Abstract

This paper presents a novel quantitative credit scoring model based on support vector machine (SVM) with adaptive genetic algorithm, gr-GA-SVM. In this study, two real world credit datasets in the University of California Irvine Machine Learning Repository are selected for the numerical experiments. SVM, GA-SVM and gr-GA-SVM, are employed to predict the accuracy of credit scoring in two datasets. Numerical results indicate that gr-GA-SVM is more accurate and efficient than SVM and GA-SVM.