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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. Pei, Youquan & Peng, Heng & Xu, Jinfeng, 2024. "A latent class Cox model for heterogeneous time-to-event data," Journal of Econometrics, Elsevier, vol. 239(2).
    2. 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.
    3. Xue Wen Tan & Stanley Kok, 2024. "Explainable Risk Classification in Financial Reports," Papers 2405.01881, arXiv.org, revised May 2024.
    4. Nartey Menzo, Benjamin Prince & Mogre, Diana & Asuamah Yeboah, Samuel, 2024. "Beyond Income: The Complexities of Credit Risk in Developing Countries," MPRA Paper 122364, University Library of Munich, Germany, revised 20 Sep 2024.
    5. Mark Potanin & Andrey Chertok & Konstantin Zorin & Cyril Shtabtsovsky, 2023. "Startup success prediction and VC portfolio simulation using CrunchBase data," Papers 2309.15552, arXiv.org.
    6. Brygała Magdalena & Korol Tomasz, 2024. "Personal bankruptcy prediction using machine learning techniques," Economics and Business Review, Sciendo, vol. 10(2), pages 118-142.
    7. Tanja Verster & Erika Fourie, 2023. "The Changing Landscape of Financial Credit Risk Models," IJFS, MDPI, vol. 11(3), pages 1-15, August.
    8. 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.
    9. 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.
    10. 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, revised Jun 2024.

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