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A novel deep learning approach to enhance creditworthiness evaluation and ethical lending practices in the economy

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Abstract

Evaluating a borrower's creditworthiness and enabling ethical lending practices are two of the most essential functions of credit scoring, making it an integral part of the economy. Credit risk management is an essential aspect of the financial industry, with the primary goal of minimising potential losses caused by customers failing to meet their credit responsibilities, such as fails to pay and bankruptcies. This risk is inherent in lending activities, where lenders extend credit to individuals or businesses. The traditional credit scoring approaches, which rely on statistical and machine learning techniques to analyse complex data and non-linear correlations in credit data has to be improved. Because the current financial sector lacks credit scoring, a deep learning network-based credit ranking model is presented in this research. This paper applies the complicated field of deep learning known as the stacked unidirectional and bidirectional long short-term memory model in the network to resolve credit scoring issues. Since scoring is not a time sequence issue, the suggested model uses the three-layer stacked LSTM and bidirectional LSTM architecture by modelling public datasets in a new way. Our suggested models beat state-of-the-art, considerably more difficult deep learning methods, proving that we could keep complexity to a minimum. The research findings indicate that the model demonstrates high levels of accuracy across various datasets. The model obtains an accuracy of 99.5% on the Australian dataset, 99.4% on the German dataset (categorical), 99.7% on the German dataset (numerical), 99.2% on the Japanese dataset, and 99.8% on the Taiwanese dataset. These results highlight the robustness and effectiveness of the model in accurately predicting outcomes for different geographical regions.

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Data availability

The datasets generated during and/or analysed during the current study are available in the [kaggle] repository, German: [http://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29].

or Kaggle url: https://www.kaggle.com/uciml/german-credit. Australian: [http://archive.ics.uci.edu/ml/datasets/Statlog+%28Australian+Credit+Approval%29]. Japan: [http://archive.ics.uci.edu/ml/datasets/Japanese+Credit+Screening]. Taiwan: [http://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients]. or Kaggle [https://www.kaggle.com/uciml/default-of-credit-card-clients-dataset].

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Funding

National Natural Science Foundation of China (General Program) (72372073) The University Philosophy and Social Science Major Fund Project in Jiangsu (2023SJZD061) Start-up Fund for Talented Researchers of Nanjing Vocational University of Industry Technology (2022SKYJ03). Besides, Nisreen Innab, would like to express sincere gratitute to AlMaarefa University, Riyadh, Saudi Arabia, for supporting this research. Authors also would like to appreciate the University Philosophy and Social Science Major Fund Project in Jiangsu Province (2023SJZD061).

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Authors and Affiliations

  1. Business School, Nanjing University, Nanjing, 210000, China

    Xiaoyan Qian

  2. School of Economics and Management, Nanjing Vocational University of Industry Technology, Nanjing, 210000, China

    Xiaoyan Qian

  3. Middlesex University Business School, London, NW4 2BT, UK

    Helen Huifen Cai

  4. Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, P.O. Box 71666, Riyadh, 11597, Saudi Arabia

    Nisreen Innab

  5. School of Continuing Education, Wenzhou Business College, Wenzhou, 325035, China

    Danni Wang

  6. Department of Economics and Political Sciences, University of Aosta Valley, Aosta, Italy

    Tiziana Ciano

  7. Decisions Lab, Mediterranea University of Reggio Calabria, Reggio Calabria, Italy

    Ali Ahmadian

  8. Faculty of Engineering and Natural Sciences, Istanbul Okan University, Istanbul, Turkey

    Ali Ahmadian

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  1. Xiaoyan Qian
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Correspondence to Danni Wang.

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Qian, X., Cai, H.H., Innab, N. et al. A novel deep learning approach to enhance creditworthiness evaluation and ethical lending practices in the economy. Ann Oper Res (2024). https://doi.org/10.1007/s10479-024-05849-1

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