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Indexed by:期刊论文

Date of Publication:2017-01-01

Journal:JOURNAL OF BUSINESS ECONOMICS AND MANAGEMENT

Included Journals:SSCI、SCIE、Scopus

Volume:18

Issue:2

Page Number:224-240

ISSN No.:1611-1699

Key Words:credit prediction; neural networks; Multi-Layer Perceptron; hidden neurons; alteration experiments; investigation and optimization

Abstract:This study proposes an investigation and optimization of Multi-Layer Perceptron (MLP) based artificial neural networks (ANN) credit prediction model, combine with the effect of different ratios of training to testing instances over five real-world credit databases. As an outcome from the alteration procedure, three different types of hidden units [K = 9 (ANN-1), K = 10 (ANN-2), K = 23 (ANN-3)] are chosen through the pilot experiments and execute, therefore, 45 (5x3x3) unique neural models. Experimental results indicate that "the neural architecture with ten hidden units" is proposed as an optimal approach to classifying the credit information. With these contributions, therefore, we complement previous evidence and modernize the methods of credit prediction modeling. This study, however, has realistic implications for bank managers and other stakeholders to delineate the risk profile of the credit customers.