A Modified Support Vector Machine model for Credit Scoring

Hui Fu,Weiwei Lin,Xiaoyong Liu

doi:10.2991/ijcis.2010.3.6.10

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.

Highlights

Credit scoring has become a very important task because credit cards are widely used by customers all over the world
Support vector machines[ ][ ][ ](SVM) are a set of related supervised learning methods used for classification and regression
In order to use support vector machine (SVM) to solve credit scoring problems on two datasets that is nonlinearly separable, we first choose a radial basis kernel because we find that SVM based on radial basis kernel is faster than SVM based on polynomial kernel

Summary

Introduction

Credit scoring has become a very important task because credit cards are widely used by customers all over the world. Credit scoring is a method of evaluating the credit risk of loan applications. Using historical data and statistical techniques, credit scoring tries to isolate the effects of various applicant characteristics on delinquencies and defaults. To build a scoring model, or "scorecard", developers analyze historical data on the performance of previously made loans to determine which borrower characteristics are useful in predicting whether the loan performed well. Even a good scoring system won’t predict with certainty any individual loan’s performance, but it should give a fairly accurate prediction of the likelihood that a loan applicant with certain characteristics will default. To build a good scoring model, developers need sufficient historical data, which reflect loan performance in periods of both good and bad economic conditions.[1][2]

Methods

Results

Conclusion