Differentiating between good credits and bad credits using neuro-fuzzy systems

To evaluate consumer loan applications, loan officers use many techniques such as judgmental systems, statistical models, or simply intuitive experience. In recent years, fuzzy systems and neural networks have attracted the growing interest of researchers and practitioners. This study compares the p...

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Bibliographic Details
Published in:European journal of operational research 2002, Vol.136 (1), p.190-211
Main Authors: Malhotra, Rashmi, Malhotra, D.K.
Format: Article
Language:English
Subjects:
Comparative analysis
Computational methods
Consumer credit
Consumer loans
Discriminant analysis
Fuzzy control
Loans
Neural networks
Neuro-fuzzy systems
Operations research
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Summary:To evaluate consumer loan applications, loan officers use many techniques such as judgmental systems, statistical models, or simply intuitive experience. In recent years, fuzzy systems and neural networks have attracted the growing interest of researchers and practitioners. This study compares the performance of artificial neuro-fuzzy inference systems (ANFIS) and multiple discriminant analysis models to screen potential defaulters on consumer loans. Using a modeling sample and a test sample, we find that the neuro-fuzzy system performs better than the multiple discriminant analysis approach to identify bad credit applications. Further, neuro-fuzzy systems have many advantages over traditional computational methods. Neuro-fuzzy system models are flexible, more tolerant of imprecise data, and can model non-linear functions of arbitrary complexity.
ISSN:0377-2217
1872-6860
DOI:10.1016/S0377-2217(01)00052-2