Evaluating consumer loans using neural networks

A number of credit-scoring models that accurately classify consumer loan applications have been developed to aid traditional judgmental methods. This study compares the performance of multiple discriminant analysis (MDA) and neural networks in identifying potential loan. The neural network models co...

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Bibliographic Details
Published in:Omega (Oxford) 2003-04, Vol.31 (2), p.83-96
Main Authors: Malhotra, Rashmi, Malhotra, D.K
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Connectionism. Neural networks
Credit
Credit risk
Credit unions
Discriminant analysis
Exact sciences and technology
Loans
Neural networks
Neural networks Credit risk Credit unions
Operational research and scientific management
Operational research. Management science
Portfolio theory
Studies
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Summary:A number of credit-scoring models that accurately classify consumer loan applications have been developed to aid traditional judgmental methods. This study compares the performance of multiple discriminant analysis (MDA) and neural networks in identifying potential loan. The neural network models consistently perform better than the MDA models in identifying potential problem loans. To alleviate the problem of bias in the training set and to examine the robustness of neural network classifiers in identifying problem loans, we cross-validate our results through seven different samples of the data.
ISSN:0305-0483
1873-5274
DOI:10.1016/S0305-0483(03)00016-1