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Toward interpretable credit scoring: integrating explainable artificial intelligence with deep learning for credit card default prediction

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Abstract

In recent years, the increasing prevalence of credit card usage has raised concerns about accurately predicting and managing credit card defaults. While machine learning and deep learning methods have shown promising results in default prediction, the black-box nature of these models often limits their interpretability and practical adoption. This study presents a new method for predicting credit card default using a combination of deep learning and explainable artificial intelligence (XAI) techniques. Integrating these methods aims to improve the interpretability of the decision-making process involved in credit card default prediction. The proposed approach is evaluated using a real-world dataset and compared to existing state-of-the-art models. Results show that the proposed approach achieves competitive prediction accuracy while providing meaningful insights into the factors driving credit card default risk. The present investigation adds to the increasing body of literature on explainable artificial intelligence (AI) in the realm of finance. Besides, it provides a pragmatic approach to assessing credit risk, balancing precision and comprehensibility. In conclusion, the model demonstrates strong potential as a credit risk assessment tool, with an accuracy of 0.8350, sensitivity of 0.8823, and specificity of 0.9879. Among the most important features identified by the model are payment delays and outstanding bill amounts. This study is a step toward more interpretable and transparent credit scoring models.

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Data availability

UCI Machine Learning Repository. (2013) [15]. Default of Credit Card Clients Dataset. Retrieved from https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients#.

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Funding

The authors received no specific funding for this study.

Author information

Authors and Affiliations

  1. Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, 33516, Egypt

    Fatma M. Talaat

  2. Faculty of Computer Science & Engineering, New Mansoura University, Gamasa, 35712, Egypt

    Fatma M. Talaat

  3. Nile Higher Institute for Engineering and Technology, Mansoura, Egypt

    Fatma M. Talaat

  4. Department of Management, College of Business Administration in Yanbu, Taibah University, 41411, Al-Madinah Al-Munawarah, Saudi Arabia

    Abdussalam Aljadani

  5. Computer Science and Information Department, Applied College, Taibah University, 46537, Madinah, Saudi Arabia

    Mahmoud Badawy

  6. Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt

    Mahmoud Badawy & Mostafa Elhosseini

  7. College of Computer Science and Engineering, Taibah University, 46421, Yanbu, Saudi Arabia

    Mostafa Elhosseini

Authors
  1. Fatma M. Talaat
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  2. Abdussalam Aljadani
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  3. Mahmoud Badawy
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  4. Mostafa Elhosseini
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Contributions

Fatma, Abdussalam, Mahmoud, and Mostafa collaborated collaboratively. Fatma and Mostafa came up with the idea and wrote the abstract and the proposal, while Mahmoud and Abdussalam contributed by comparing and doing the experiments.

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Correspondence to Fatma M. Talaat.

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Talaat, F.M., Aljadani, A., Badawy, M. et al. Toward interpretable credit scoring: integrating explainable artificial intelligence with deep learning for credit card default prediction. Neural Comput & Applic 36, 4847–4865 (2024). https://doi.org/10.1007/s00521-023-09232-2

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  • DOI: https://doi.org/10.1007/s00521-023-09232-2

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