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Credit Scoring Model Based on HMM/Baum-Welch Method

Author

Listed:
  • Badreddine Benyacoub

    (Institut National de Statistique et d’Economie Appliquée)

  • Souad ElBernoussi

    (University Mohammed V)

  • Abdelhak Zoglat

    (University Mohammed V)

  • Mohamed Ouzineb

    (Institut National de Statistique et d’Economie Appliquée)

Abstract

Credit scoring becomes an important task to evaluate an applicant by a banker. Many tools are available for making initial lending decisions. This paper presents a Hidden Markov Model (HMM ) for credit scoring, and uses Baum-Welch method; an iterative procedure approach; for building a set of credit scoring models. We introduce HMM/Baum-Welch model: a tool developed to explore a good accurate model for classification problems. There are two phases in this model: learned an initial model from training data using HMM, and re-estimating HMM parameters by an iterative process using Baum-Welch algorithm. The proposed model is successfully applied to a real credit problem, and the application procedure is illustrated through two data sets: German and Australian. The criteria used to evaluate the performance of different resulting models are the accuracy and AUC (area under the ROC curve). The experiment of this model, shows that, HMM with Baum-Welch approach can improve the pattern classification performance in credit scoring.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:compec:v:59:y:2022:i:3:d:10.1007_s10614-021-10122-9
    DOI: 10.1007/s10614-021-10122-9
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    References listed on IDEAS

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    1. Wiginton, John C., 1980. "A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 15(3), pages 757-770, September.
    2. Desai, Vijay S. & Crook, Jonathan N. & Overstreet, George A., 1996. "A comparison of neural networks and linear scoring models in the credit union environment," European Journal of Operational Research, Elsevier, vol. 95(1), pages 24-37, November.
    3. 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.
    4. Nicky J. Welton & Howard H. Z. Thom, 2015. "Value of Information," Medical Decision Making, , vol. 35(5), pages 564-566, July.
    5. 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.
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    Cited by:

    1. 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.

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