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 (5×3×3) 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.
Highlights
Credit prediction is a key application in statistical modeling and plays an important function in contemporary financial risk management practice
Zhao et al (2015) demonstrated that the classification performance of Multi-Layer Perceptron (MLP) based neural networks (NN) methods could significantly improve by changing the ratio of sample composite mixture (SCM) of training and testing instances, the number of hidden neurons, and the training iterations
We use three different types of hidden units 9 (ANN–1), 10 (ANN–2), 23 (ANN–3), those are picked through pilot studies and execute, 45 (5×3×3) unique neural models
Summary
Credit prediction is a key application in statistical modeling and plays an important function in contemporary financial risk management practice. Zhao et al (2015) demonstrated that the classification performance of MLP based NN methods could significantly improve by changing the ratio of sample composite mixture (SCM) of training and testing instances, the number of hidden neurons, and the training iterations It depends on chosen real world databases for training and validating the trained neural model, Khashman (2011) added. For the competitive performance of NN model, versatile databases with different ratios of SCM in training-testing examples, an optimum selection of hidden neurons during the model construction phase must be cautiously refined In these contexts, this study proposes an investigation and optimization of MLP based NN credit prediction model, combine with the effect of different ratios of training to testing datasets. We complement previous evidence and modernize the methods of credit prediction modeling
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