Data Analytics for Developing and Validating Credit Models

  • Michalis Doumpos
  • Christos Lemonakis
  • Dimitrios Niklis
  • Constantin Zopounidis
Part of the EURO Advanced Tutorials on Operational Research book series (EUROATOR)


The development of credit risk assessment models in the context of credit scoring and rating, is a data-intensive task that involves a considerable level of sophistication in terms of data preparation, analysis, and modeling. From a data analytics perspective, the construction of credit scoring and rating models can be considered as a classification task, that requires the development of models differentiating the borrowers by their level of credit risk. The model fitting process can be implemented with various methodological approaches, based on different types of models, model fitting criteria, and estimation procedures. This chapter presents an overview of different analytical modeling techniques from various fields, such as statistical models (naïve Bayes classifier, discriminant analysis, logistic regression), machine learning (classification trees, neural networks, ensembles), and multicriteria decision aid (value function models and outranking models). Moreover, performance measurement issues are discussed, focusing on the presentation of various popular metrics for evaluating the predictive power and information value of credit scoring and rating models.


  1. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609.CrossRefGoogle Scholar
  2. Altman, E. I., Haldeman, R. G., & Narayanan, P. (1977). ZETATM analysis – A new model to identify bankruptcy risk of corporations. Journal of Banking and Finance, 1(1), 29–54.CrossRefGoogle Scholar
  3. Baesens, B., Setiono, R., Mues, C., & Vanthienen, J. (2003). Using neural network rule extraction and decision tables for credit-risk evaluation. Management Science, 49(3), 312–329.CrossRefGoogle Scholar
  4. Blöchlinger, A., & Leippold, M. (2006). Economic benefit of powerful credit scoring. Journal of Banking and Finance, 30(3), 851–873.CrossRefGoogle Scholar
  5. Bugera, V., Konno, H., & Uryasev, S. (2002). Credit cards scoring with quadratic utility functions. Journal of Multi-Criteria Decision Analysis, 11(4–5), 197–211.CrossRefGoogle Scholar
  6. Desai, V. S., Crook, J. N., & Overstreet, G. A. (1996). A comparison of neural networks and linear scoring models in the credit union environment. European Journal of Operational Research, 95(1), 24–37.CrossRefGoogle Scholar
  7. Dimitras, A. I., Zanakis, S. H., & Zopounidis, C. (1996). A survey of business failures with an emphasis on prediction methods and industrial applications. European Journal of Operational Research, 90(3), 487–513.CrossRefGoogle Scholar
  8. Doumpos, M., & Pasiouras, F. (2005). Developing and testing models for replicating credit ratings: A multicriteria approach. Computational Economics, 25(4), 327–341.CrossRefGoogle Scholar
  9. Doumpos, M., & Zopounidis, C. (2011). A multicriteria ouranking modeling approach for credit rating. Decision Sciences, 42(3), 721–742.CrossRefGoogle Scholar
  10. Doumpos, M., Kosmidou, K., Baourakis, G., & Zopounidis, C. (2002). Credit risk assessment using a multicriteria hierarchical discrimination approach: A comparative analysis. European Journal of Operational Research, 138(2), 392–412.CrossRefGoogle Scholar
  11. Frydman, H., Altman, E. I., & Kao, D.-L. (1985). Introducing recursive partitioning for financial classification: The case of financial distress. The Journal of Finance, 40(1), 269.CrossRefGoogle Scholar
  12. Galindo, J., & Tamayo, P. (2000). Credit risk assessment using statistical and machine learning: Basic methodology and risk modeling applications. Computational Economics, 15(1/2), 107–143.CrossRefGoogle Scholar
  13. Lessmann, S., Baesens, B., Seow, H.-V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. European Journal of Operational Research, 247(1), 124–136.CrossRefGoogle Scholar
  14. Martens, D., Baesens, B., Van Gestel, T., & Vanthienen, J. (2007). Comprehensible credit scoring models using rule extraction from support vector machines. European Journal of Operational Research, 183(3), 1466–1476.CrossRefGoogle Scholar
  15. Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109.CrossRefGoogle Scholar
  16. Oliver, R. M. (2013). Financial performance measures in credit scoring. EURO Journal on Decision Processes, 1(3–4), 169–185.CrossRefGoogle Scholar
  17. Papageorgiou, D., Doumpos, M., Zopounidis, C., & Pardalos, P. M. (2008). Credit rating systems: Regulatory framework and comparative evaluation of existing methods. In C. Zopounidis, M. Doumpos, & P. M. Pardalos (Eds.), Handbook of financial engineering (pp. 457–488). New York: Springer.CrossRefGoogle Scholar
  18. Piramuthu, S. (1999). Financial credit-risk evaluation with neural and neurofuzzy systems. European Journal of Operational Research, 112(2), 310–321.CrossRefGoogle Scholar
  19. Srinivasan, V., & Kim, Y. H. (1987). Credit granting: A comparative analysis of classification procedures. The Journal of Finance, 42(3), 665–681.CrossRefGoogle Scholar
  20. Tabouratzi, E., Lemonakis, C., & Garefalakis, A. (2017). Determinants of failure in greek manufacturing SMEs. Corporate Ownership and Control, 14(3), 45–50.CrossRefGoogle Scholar
  21. West, D. (2000). Neural network credit scoring models. Computers and Operations Research, 27(11–12), 1131–1152.CrossRefGoogle Scholar
  22. Zavgren, C. V. (1985). Assessing the vulnerability to failure of American industrial firms: A logistic analysis. Journal of Business Finance and Accounting, 12(1), 19–45.CrossRefGoogle Scholar
  23. Zhang, Z., Gao, G., & Shi, Y. (2014). Credit risk evaluation using multi-criteria optimization classifier with kernel, fuzzification and penalty factors. European Journal of Operational Research, 237(1), 335–348.CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Michalis Doumpos
    • 1
  • Christos Lemonakis
    • 2
  • Dimitrios Niklis
    • 3
  • Constantin Zopounidis
    • 1
    • 4
  1. 1.School of Production Engineering and ManagementTechnical University of CreteChaniaGreece
  2. 2.Department of Business ManagementUniversity of Applied Sciences CreteCreteGreece
  3. 3.Department of Accounting and FinanceWestern Macedonia University of Applied SciencesKozaniGreece
  4. 4.Audencia Business SchoolInstitute of FinanceNantesFrance

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