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Credit Risk Scoring with Bayesian Network Models

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  • Chee Kian Leong

    (University of Nottingham, Ningbo)

Abstract

This paper proposes a Bayesian network model to address censoring, class imbalance and real-time implementation issues in credit risk scoring. It shows that the Bayesian network model performs well against competing models (logistic regression model and neural network model) along several dimensions such as accuracy, sensitivity, precision and the receiver characteristic curve. Better performance of the Bayesian network model is particularly salient with class imbalance, higher dimensions and a rejection sample. Furthermore, the Bayesian network model can be scaled efficiently when implemented onto a larger dataset, thus making it amenable for real-time implementation.

Suggested Citation

  • Chee Kian Leong, 2016. "Credit Risk Scoring with Bayesian Network Models," Computational Economics, Springer;Society for Computational Economics, vol. 47(3), pages 423-446, March.
    • Handle: RePEc:kap:compec:v:47:y:2016:i:3:d:10.1007_s10614-015-9505-8
    DOI: 10.1007/s10614-015-9505-8
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    References listed on IDEAS

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    1. Pesaran, Hashem & Timmermann, Allan, 2005. "Real-Time Econometrics," Econometric Theory, Cambridge University Press, vol. 21(1), pages 212-231, February.
    2. D. J. Hand & W. E. Henley, 1997. "Statistical Classification Methods in Consumer Credit Scoring: a Review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 523-541, September.
    3. David J Hand & Niall M Adams, 2014. "Selection bias in credit scorecard evaluation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 408-415, March.
    4. Yuliya Demyanyk & Otto Van Hemert, 2011. "Understanding the Subprime Mortgage Crisis," The Review of Financial Studies, Society for Financial Studies, vol. 24(6), pages 1848-1880.
    5. William H. Greene, 1992. "A Statistical Model for Credit Scoring," Working Papers 92-29, New York University, Leonard N. Stern School of Business, Department of Economics.
    6. Boyes, William J. & Hoffman, Dennis L. & Low, Stuart A., 1989. "An econometric analysis of the bank credit scoring problem," Journal of Econometrics, Elsevier, vol. 40(1), pages 3-14, January.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Li, Yibei & Wang, Ximei & Djehiche, Boualem & Hu, Xiaoming, 2020. "Credit scoring by incorporating dynamic networked information," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1103-1112.
    2. Junlong Peng & Jing Zhou & Fanyi Meng & Yan Yu, 2021. "Analysis on the hidden cost of prefabricated buildings based on FISM-BN," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-20, June.
    3. Runchi Zhang & Zhiyi Qiu, 2020. "Optimizing hyper-parameters of neural networks with swarm intelligence: A novel framework for credit scoring," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-35, June.
    4. Chi Ming Chen & Geoffrey Kwok Fai Tso & Kaijian He, 2024. "Quantum Optimized Cost Based Feature Selection and Credit Scoring for Mobile Micro-financing," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 919-950, February.
    5. Badreddine Benyacoub & Souad ElBernoussi & Abdelhak Zoglat & Mohamed Ouzineb, 2022. "Credit Scoring Model Based on HMM/Baum-Welch Method," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1135-1154, March.
    6. Michail Tsagris, 2021. "A New Scalable Bayesian Network Learning Algorithm with Applications to Economics," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 341-367, January.
    7. Michail Tsagris, 2022. "The FEDHC Bayesian Network Learning Algorithm," Mathematics, MDPI, vol. 10(15), pages 1-28, July.

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    More about this item

    Keywords

    Credit scoring; Bayesian network; Censoring; Class imbalance; Real time scoring;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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