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Modeling credit approval data with neural networks: an experimental investigation and optimization

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  • Chi Guotai
  • Mohammad Zoynul Abedin
  • Fahmida–E Moula

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.

Suggested Citation

  • Chi Guotai & Mohammad Zoynul Abedin & Fahmida–E Moula, 2017. "Modeling credit approval data with neural networks: an experimental investigation and optimization," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 18(2), pages 224-240, March.
    • Handle: RePEc:taf:jbemgt:v:18:y:2017:i:2:p:224-240
    DOI: 10.3846/16111699.2017.1280844
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    Citations

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    Cited by:

    1. Omar H. Fares & Irfan Butt & Seung Hwan Mark Lee, 2023. "Utilization of artificial intelligence in the banking sector: a systematic literature review," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 28(4), pages 835-852, December.
    2. Salman Bahoo & Marco Cucculelli & Xhoana Goga & Jasmine Mondolo, 2024. "Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis," SN Business & Economics, Springer, vol. 4(2), pages 1-46, February.
    3. Sun, Weixin & Zhang, Xuantao & Li, Minghao & Wang, Yong, 2023. "Interpretable high-stakes decision support system for credit default forecasting," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    4. Mohammad Shamsu Uddin & Guotai Chi & Mazin A. M. Al Janabi & Tabassum Habib & Kunpeng Yuan, 2022. "Modeling credit risk with a multi‐stage hybrid model: An alternative statistical approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1386-1415, November.
    5. Amine Belhadi & Sachin S. Kamble & Venkatesh Mani & Imane Benkhati & Fatima Ezahra Touriki, 2025. "An ensemble machine learning approach for forecasting credit risk of agricultural SMEs’ investments in agriculture 4.0 through supply chain finance," Annals of Operations Research, Springer, vol. 345(2), pages 779-807, February.
    6. Hornuf, Lars & Safari, Kulondwa & Voshaar, Johannes, 2025. "Mobile fintech adoption in Sub-Saharan Africa: A systematic literature review and meta-analysis," Research in International Business and Finance, Elsevier, vol. 73(PA).
    7. Weixin Sun & Heli Chen & Feng Liu & Yong Wang, 2025. "Point and interval prediction of crude oil futures prices based on chaos theory and multiobjective slime mold algorithm," Annals of Operations Research, Springer, vol. 345(2), pages 1003-1033, February.
    8. Weng, Futian & Zhu, Miao & Buckle, Mike & Hajek, Petr & Abedin, Mohammad Zoynul, 2025. "Class imbalance Bayesian model averaging for consumer loan default prediction: The role of soft credit information," Research in International Business and Finance, Elsevier, vol. 74(C).
    9. Mohammad S. Uddin & Guotai Chi & Mazin A. M. Al Janabi & Tabassum Habib, 2022. "Leveraging random forest in micro‐enterprises credit risk modelling for accuracy and interpretability," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3713-3729, July.
    10. Mohammad Zoynul Abedin & Mahmudul Hasan Moon & M. Kabir Hassan & Petr Hajek, 2025. "Deep learning-based exchange rate prediction during the COVID-19 pandemic," Annals of Operations Research, Springer, vol. 345(2), pages 1335-1386, February.

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