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Bayesian Kernel-Based Classification for Financial Distress Detection

Author

Listed:
  • T. VAN GESTEL
  • B. BAESENS
  • J. A.K. SUYKENS
  • D. VAN DEN POEL
  • D.-E. BAESTAENS
  • BM. WILLEKENS
Abstract
Corporate credit granting is a key commercial activity of financial institutions nowadays. A critical first step in the credit granting process usually involves a careful financial analysis of the creditworthiness of the potential client. Wrong decisions result either in foregoing valuable clients or, more severely, in substantial capital losses if the client subsequently defaults. It is thus of crucial importance to develop models that estimate the probability of corporate bankruptcy with a high degree of accuracy. Many studies focused on the use of financial ratios in linear statistical models, such as linear discriminant analysis and logistic regression. However, the obtained error rates are often high. In this paper, Least Squares Support Vector Machine (LS-SVM) classifiers, also known as kernel Fisher discriminant analysis, are applied within the Bayesian evidence framework in order to automatically infer and analyze the creditworthiness of potential corporate clients. The inferred posterior class probabilities of bankruptcy are then used to analyze the sensitivity of the classifier output with respect to the given inputs and to assist in the credit assignment decision making process. The suggested nonlinear kernel based classifiers yield better performances than linear discriminant analysis and logistic regression when applied to a real-life data set concerning commercial credit granting to mid-cap Belgian and Dutch firms.

Suggested Citation

  • T. Van Gestel & B. Baesens & J. A.K. Suykens & D. Van Den Poel & D.-E. Baestaens & Bm. Willekens, 2004. "Bayesian Kernel-Based Classification for Financial Distress Detection," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/247, Ghent University, Faculty of Economics and Business Administration.
    • Handle: RePEc:rug:rugwps:04/247
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    References listed on IDEAS

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    Cited by:

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    2. Martens, David & Baesens, Bart & Van Gestel, Tony & Vanthienen, Jan, 2007. "Comprehensible credit scoring models using rule extraction from support vector machines," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1466-1476, December.
    3. José Manuel Maside-Sanfiz & María-Celia López-Penabad & Ana Iglesias-Casal & Juan Torrelles Manent, 2024. "Determinants of the profitability of Sheltered Workshops: efficiency and effects of the COVID-19 crisis," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.
    4. Li, Hui & Hong, Lu-Yao & He, Jia-Xun & Xu, Xuan-Guo & Sun, Jie, 2013. "Small sample-oriented case-based kernel predictive modeling and its economic forecasting applications under n-splits-k-times hold-out assessment," Economic Modelling, Elsevier, vol. 33(C), pages 747-761.
    5. Huang, Chao & Dai, Chong & Guo, Miao, 2015. "A hybrid approach using two-level DEA for financial failure prediction and integrated SE-DEA and GCA for indicators selection," Applied Mathematics and Computation, Elsevier, vol. 251(C), pages 431-441.
    6. Polyzos, Stathis & Samitas, Aristeidis & Katsaiti, Marina-Selini, 2020. "Who is unhappy for Brexit? A machine-learning, agent-based study on financial instability," International Review of Financial Analysis, Elsevier, vol. 72(C).
    7. Swati Anand & Kushendra Mishra, 2022. "Identifying potential millennial customers for financial institutions using SVM," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 27(4), pages 335-345, December.
    8. Lean Yu & Xinxie Li & Ling Tang & Zongyi Zhang & Gang Kou, 2015. "Social credit: a comprehensive literature review," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 1(1), pages 1-18, December.
    9. Li, Hui & Sun, Jie, 2012. "Forecasting business failure: The use of nearest-neighbour support vectors and correcting imbalanced samples – Evidence from the Chinese hotel industry," Tourism Management, Elsevier, vol. 33(3), pages 622-634.
    10. Ding, Shusheng & Cui, Tianxiang & Bellotti, Anthony Graham & Abedin, Mohammad Zoynul & Lucey, Brian, 2023. "The role of feature importance in predicting corporate financial distress in pre and post COVID periods: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 90(C).
    11. Yu Zhao & Huaming Du & Qing Li & Fuzhen Zhuang & Ji Liu & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised May 2023.
    12. Ching-Hsue Cheng & Ssu-Hsiang Wang, 2015. "A quarterly time-series classifier based on a reduced-dimension generated rules method for identifying financial distress," Quantitative Finance, Taylor & Francis Journals, vol. 15(12), pages 1979-1994, December.
    13. Lessmann, Stefan & Voß, Stefan, 2009. "A reference model for customer-centric data mining with support vector machines," European Journal of Operational Research, Elsevier, vol. 199(2), pages 520-530, December.
    14. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, November.
    15. Geng, Ruibin & Bose, Indranil & Chen, Xi, 2015. "Prediction of financial distress: An empirical study of listed Chinese companies using data mining," European Journal of Operational Research, Elsevier, vol. 241(1), pages 236-247.
    16. Fahmida E. Moula & Chi Guotai & Mohammad Zoynul Abedin, 2017. "Credit default prediction modeling: an application of support vector machine," Risk Management, Palgrave Macmillan, vol. 19(2), pages 158-187, May.
    17. Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.
    18. L C Thomas, 2010. "Consumer finance: challenges for operational research," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 41-52, January.
    19. Brandner, Hubertus & Lessmann, Stefan & Voß, Stefan, 2013. "A memetic approach to construct transductive discrete support vector machines," European Journal of Operational Research, Elsevier, vol. 230(3), pages 581-595.
    20. Qifeng Qiao & Peter A. Beling, 2016. "Decision analytics and machine learning in economic and financial systems," Environment Systems and Decisions, Springer, vol. 36(2), pages 109-113, June.

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    Keywords

    Credit Scoring; Kernel Fisher Discriminant Analysis; Least Squares Support Vector Machine Classifiers; Bayesian Inference;
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