Credit scoring algorithm based on link analysis ranking with support vector machine

Authors:
Xiujuan Xu;Chunguang Zhou;Zhe Wang
Affiliations:
College of Computer Science, Key Laboratory of Symbol Computation and Knowledge, Engineering of the Ministry of Education, Jilin University, Changchun 130012, China;College of Computer Science, Key Laboratory of Symbol Computation and Knowledge, Engineering of the Ministry of Education, Jilin University, Changchun 130012, China;College of Computer Science, Key Laboratory of Symbol Computation and Knowledge, Engineering of the Ministry of Education, Jilin University, Changchun 130012, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

Credit scoring is very important in business, especially in banks. We want to describe a person who is a good credit or a bad one by evaluating his/her credit. We systematically proposed three link analysis algorithms based on the preprocess of support vector machine, to estimate an applicant's credit so as to decide whether a bank should provide a loan to the applicant. The proposed algorithms have two major phases which are called input weighted adjustor and class by support vector machine-based models. In the first phase, we consider the link relation by link analysis and integrate the relation of applicants through their information into input vector of next phase. In the other phase, an algorithm is proposed based on general support vector machine model. A real world credit dataset is used to evaluate the performance of the proposed algorithms by 10-fold cross-validation method. It is shown that the genetic link analysis ranking methods have higher performance in terms of classification accuracy.