A Soft Intelligent Risk Evaluation Model for Credit Scoring Classification

International Journal of Financial Studies

Mehdi Khashei¹  Akram Mirahmadi² 

[1] Department of Industrial Engineering, Isfahan University of Technology (IUT), Isfahan 84156-83111, Iran; E-Mail

关键词: risk management;    classification;    credit scoring;    soft computing techniques;    artificial intelligent;    Multi-Layer Perceptrons (MLPs);   

DOI  :  10.3390/ijfs3030411

来源: mdpi

【 摘 要 】

Risk management is one of the most important branches of business and finance. Classification models are the most popular and widely used analytical group of data mining approaches that can greatly help financial decision makers and managers to tackle credit risk problems. However, the literature clearly indicates that, despite proposing numerous classification models, credit scoring is often a difficult task. On the other hand, there is no universal credit-scoring model in the literature that can be accurately and explanatorily used in all circumstances. Therefore, the research for improving the efficiency of credit-scoring models has never stopped. In this paper, a hybrid soft intelligent classification model is proposed for credit-scoring problems. In the proposed model, the unique advantages of the soft computing techniques are used in order to modify the performance of the traditional artificial neural networks in credit scoring. Empirical results of Australian credit card data classifications indicate that the proposed hybrid model outperforms its components, and also other classification models presented for credit scoring. Therefore, the proposed model can be considered as an appropriate alternative tool for binary decision making in business and finance, especially in high uncertainty conditions.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.