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Credit risk detection based on machine learning algorithms

Author

Listed:
  • Xin Wang
  • Kai Zong
  • Cuicui Luo

Abstract

As the global economic environment has become more complicated in recent years, more and more credit bonds have defaulted. The credit risk early warning model plays a very effective role in preventing and controlling financial risk and debt default. This paper uses machine learning methods to establish a credit default risk prediction framework. In this paper, the oversampling technique is first applied to deal with imbalanced credit default data sets and then the credit risk detection performance of several machine learning algorithms is compared. The empirical results show that the performance of the ensemble learning algorithms is the best.

Suggested Citation

  • Xin Wang & Kai Zong & Cuicui Luo, 2022. "Credit risk detection based on machine learning algorithms," International Journal of Financial Services Management, Inderscience Enterprises Ltd, vol. 11(3), pages 183-189.
    • Handle: RePEc:ids:ijfsmg:v:11:y:2022:i:3:p:183-189
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    Citations

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    Cited by:

    1. Flavio Bazzana & Marco Bee & Ahmed Almustfa Hussin Adam Khatir, 2024. "Machine learning techniques for default prediction: an application to small Italian companies," Risk Management, Palgrave Macmillan, vol. 26(1), pages 1-23, February.
    2. Mark Potanin & Andrey Chertok & Konstantin Zorin & Cyril Shtabtsovsky, 2023. "Startup success prediction and VC portfolio simulation using CrunchBase data," Papers 2309.15552, arXiv.org.
    3. Tanja Verster & Erika Fourie, 2023. "The Changing Landscape of Financial Credit Risk Models," IJFS, MDPI, vol. 11(3), pages 1-15, August.
    4. Anton van Dyk & Gary van Vuuren, 2023. "Measurement and Calibration of Regulatory Credit Risk Asset Correlations," JRFM, MDPI, vol. 16(9), pages 1-19, September.
    5. Rogojan Luana Cristina & Croicu Andreea Elena & Iancu Laura Andreea, 2023. "Modern Approaches in Credit Risk Modeling: A Literature Review," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 17(1), pages 1617-1627, July.
    6. Sahab Zandi & Kamesh Korangi & Mar'ia 'Oskarsd'ottir & Christophe Mues & Cristi'an Bravo, 2024. "Attention-based Dynamic Multilayer Graph Neural Networks for Loan Default Prediction," Papers 2402.00299, arXiv.org.

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