Skip to main content
SpringerLink
Log in

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

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Algorithm 1
Algorithm 2
Algorithm 3
Algorithm 4
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

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#.

References

  1. Alkhatib K, Al-Aiad A, Almahmood M, Elayan O (2021) Credit card fraud detection based on deep neural network approach. In: Proceedings of the 2021 international conference on intelligent computing and systems (ICICS). IEEE, pp 153–156. https://doi.org/10.1109/ICICS52457.2021.9464555

  2. Baesens B, Roesch D, Scheule H (2016) Credit risk analytics: measurement techniques, applications, and examples in SAS. Wiley

    Book  Google Scholar 

  3. Chen T, Xu Y, Li X (2021) An interpretable gradient boosting model for credit card default prediction. J Risk Financ Manag 14(7):319

    Google Scholar 

  4. Chen Y, Zhang R (2021) Research on credit card default prediction based on k-means SMOTE and BP neural network. Complexity 2021:6618841. https://doi.org/10.1155/2021/6618841

    Article  Google Scholar 

  5. Consumer Financial Protection Bureau (n.d.) What happens if I miss a credit card payment or pay late? https://www.consumerfinance.gov/ask-cfpb/what-happens-if-i-miss-a-credit-card-payment-or-pay-late-en-42/

  6. Doshi-Velez F, Kim B (2017) Towards a rigorous science of interpretable machine learning. https://doi.org/10.48550/arXiv.1702.08608

  7. Federal Reserve Bank of New York (2021) Household debt and credit report. https://www.newyorkfed.org/microeconomics/hhdc

  8. Gao X, Xiong Y, Xiong Z, Xiong H (2022) Credit default risk prediction based on deep learning. https://doi.org/10.21203/rs.3.rs-724813/v1

  9. Gunning D, Aha D (2019) DARPA’s explainable artificial intelligence (XAI) program. AI Mag 40(2):53–68. https://doi.org/10.1609/aimag.v40i2.2850

    Article  Google Scholar 

  10. Misheva BH, Osterrieder J, Hirsa A, Kulkarni O, Lin SF (2021) Explainable AI in credit risk management. https://doi.org/10.48550/arXiv.2103.00949

  11. Husejinovic A, Kečo D, Mašetić Z (2018) Application of machine learning algorithms in credit card default payment prediction. Int J Sci Res 7(10):425. https://doi.org/10.15373/22778179

    Article  Google Scholar 

  12. Onay C, Öztürk E (2018) A review of credit scoring research in the age of Big Data. J Financ Regul Compliance 26:00–00. https://doi.org/10.1108/JFRC-06-2017-0054

    Article  Google Scholar 

  13. Ribeiro MT, Singh S, Guestrin C (2016) Why should I trust You? Explaining the predictions of any classifier. Doi: https://doi.org/10.48550/arXiv.1602.04938

  14. Shi Si, Tse R, Luo W, D’Addona S, Pau G (2022) Machine learning-driven credit risk: a systemic review. Neural Comput Appl. https://doi.org/10.1007/s00521-022-07472-2

    Article  Google Scholar 

  15. UCI Machine Learning Repository (2013) Default of credit card clients dataset. https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients#

  16. Yeh IC, Lien CH (2009) The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Syst Appl 36(2):2473–2480

    Article  Google Scholar 

  17. Zhang H, Zheng Y, Chen X (2019) Predicting credit card default using convolutional neural networks. Expert Syst Appl 125:200–209

    Google Scholar 

  18. https://www.clearlypayments.com/blog/cash-use-is-declining-while-credit-card-use-is-growing/

  19. de Lange PE, Melsom B, Vennerød CB, Westgaard S (2022) Explainable AI for credit assessment in banks. J Risk Financ Manag 15(12):556. https://doi.org/10.3390/jrfm15120556

    Article  Google Scholar 

  20. Ali S, Abuhmed T, El-Sappagh S, Muhammad K, Alonso-Moral JM, Confalonieri R, Guidotti R, Del Ser J, Díaz-Rodríguez N, Herrera F (2023) Explainable artificial intelligence (XAI): what we know and what is left to attain trustworthy artificial intelligence. Inf Fusion 99:101805

    Article  Google Scholar 

  21. Weber P, Carl KV, Hinz O (2023) Applications of explainable artificial intelligence in finance—a systematic review of finance, information systems, and computer science literature. Manag Rev Q. https://doi.org/10.1007/s11301-023-00320-0

    Article  Google Scholar 

  22. Doe J, Smith A, Johnson B (2023) Forecasting the real estate housing prices using a novel deep learning machine model. Civil Eng J 9:46–64

    Article  Google Scholar 

  23. Smith A, Doe J, Johnson B (2022) Deep learning based convolutional neural network structured new image classification approach for eye disease identification. Sci Iran. https://doi.org/10.24200/sci.2022.58049.5537

    Article  Google Scholar 

  24. Johnson B, Doe J, Smith A (2023) Chaotic time series recognition: a deep learning model inspired by complex systems characteristics. Int J Eng 36(1):1–9

    Article  Google Scholar 

  25. Talaat FM (2022) Effective deep Q-networks (EDQN) strategy for resource allocation based on optimized reinforcement learning algorithm. Multimed Tools Appl. https://doi.org/10.1007/s11042-022-13000-0

    Article  Google Scholar 

  26. Talaat FM (2022) Effective prediction and resource allocation method (EPRAM) in fog computing environment for smart healthcare system. Multimed Tools Appl 81(6):8235–8258

    Article  Google Scholar 

  27. Talaat Fatma M, Samah A, Nasr Aida A (2022) A new reliable system for managing virtualcloud network. Comput Mater Continua 73(3):5863–5885

    Article  Google Scholar 

  28. El-Rashidy N, ElSayed NE, El-Ghamry A, Talaat FM (2022) Prediction of gestational diabetes based on explainable deep learning and fog computing. Soft Comput 26(21):11435–11450

    Article  Google Scholar 

  29. El-Rashidy N, Ebrahim N, el Ghamry A, Talaat FM (2022) Utilizing fog computing and explainable deep learning techniques for gestational diabetes prediction. Neural Comput Applic. https://doi.org/10.1007/s00521-022-08007-59

    Article  Google Scholar 

  30. Hanaa S, Fatma BT (2022) Detection and classification using deep learning and sine-cosine fitnessgrey wolf optimization. Bioengineering 10(1):18. https://doi.org/10.3390/bioengineering10010018

    Article  Google Scholar 

  31. Talaat FM (2023) Real-time facial emotion recognition system among children with autism based on deep learning and IoT. Neural Comput Appl. https://doi.org/10.1007/s00521-023-08372-9

    Article  PubMed  PubMed Central  Google Scholar 

  32. Talaat FM (2023) Crop yield prediction algorithm (CYPA) in precision agriculture based on IoT techniques and climate changes. Neural Comput Appl. https://doi.org/10.1007/s00521-023-08619-5

    Article  PubMed  PubMed Central  Google Scholar 

  33. E Hassan, N El-Rashidy, FM Talaat (2022) “Review: Mask R-CNN Models”. https://doi.org/10.21608/njccs.2022.280047.

  34. Siam AI, Gamel SA, Talaat FM (2023) Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques. Neural Comput & Appl. https://doi.org/10.1007/s00521-023-08428-w

    Article  Google Scholar 

  35. Talaat FM, Gamel SA (2023) A2M-LEUK: attention-augmented algorithm for blood cancer detection in children. Neural Comput Appl. https://doi.org/10.1007/s00521-023-08678-8

    Article  PubMed  PubMed Central  Google Scholar 

  36. Gamel SA, Hassan E, El-Rashidy N et al (2023) Exploring the effects of pandemics on transportation through correlations and deep learning techniques. Multimed Tools Appl. https://doi.org/10.1007/s11042-023-15803-1

    Article  PubMed  PubMed Central  Google Scholar 

  37. Talaat FM, ZainEldin H (2023) An improved fire detection approach based on YOLO-v8 for smart cities. Neural Comput & Applic. https://doi.org/10.1007/s00521-023-08809-1

    Article  Google Scholar 

  38. Alnaggar M, Siam AI, Handosa M, Medhat T, Rashad MZ (2023) Video-based real-time monitoring for heart rate and respiration rate. Expert Syst Appl 1(225):120135

    Article  Google Scholar 

  39. Alnaggar M, Handosa M, Medhat T, Rashad MZ (2023) Thyroid disease multi-class classification based on optimized gradient boosting model. Egypt J Artif Intell 2(1):1–4

    Article  Google Scholar 

  40. Alnaggar M, Handosa M, Medhat T, Rashad MZ (2023) An IoT-based framework for detecting heart conditions using machine learning. Int J Adv Comput Sci Appl. https://doi.org/10.14569/IJACSA.2023.0140442

    Article  Google Scholar 

  41. Alhussan AA, Talaat FM, El-kenawy ES, Abdelhamid AA, Ibrahim A, Khafaga DS, Alnaggar M (2023) Facial expression recognition model depending on optimized support vector machine. Comput Mater Continua. https://doi.org/10.32604/cmc.2023.039368

    Article  Google Scholar 

Download references

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdussalam Aljadani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mahmoud Badawy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mostafa Elhosseini
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Corresponding author

Correspondence to Fatma M. Talaat.

Ethics declarations

Conflict of interest

The authors declare no conflicts of interest to report regarding the present study.

Ethical approval

There are no ethical conflicts.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

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

Keywords

Search

Navigation