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论文类型： 期刊论文

发表时间： 2017-01-01

发表刊物： JOURNAL OF BUSINESS ECONOMICS AND MANAGEMENT

收录刊物： SSCI、SCIE、Scopus

卷号： 18

期号： 2

页面范围： 224-240

ISSN号： 1611-1699

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

摘要： 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.