Credit Risk Assessment in Commercial Banks Based on Fuzzy Support Vector Machines

Qifeng Zhou, Cheng-de Lin
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Abstract

Credit risk assessment plays an important role in banks credit risk management. The objective of credit assessment is to decide credit ranks, which denote the capacity of enterprises to meet their financial commitments. Traditional "one-versus-one" approach has been commonly used in the multi-classification method based on support vector machine (SVM). Since SVM for pattern recognition is based on binary classification, there will be unclassifiable regions when extended to multi-classification problems. Focus on this problem, a new credit risk assessment model based on fuzzy SVM is introduced in this paper that can give a reasonable classification for unclassifiable examples. Experiment results show that the fuzzy SVM method provides a better performance in generalization ability and assessment accuracy than conventional one-versus-one multi-classification approach
基于模糊支持向量机的商业银行信用风险评估
信用风险评估在银行信用风险管理中起着重要的作用。信用评价的目的是确定企业的信用等级,信用等级表示企业履行其财务承诺的能力。基于支持向量机(SVM)的多分类方法通常采用传统的“一对一”方法。由于模式识别的支持向量机是基于二值分类的,在扩展到多分类问题时会出现不可分类的区域。针对这一问题,本文提出了一种新的基于模糊支持向量机的信用风险评估模型,该模型可以对不可分类的样本进行合理的分类。实验结果表明,与传统的一对一多分类方法相比,模糊支持向量机方法具有更好的泛化能力和评价精度
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