Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
In other specialized credit granting contexts (e.g., project finance), the risk assessment process is mostly based on empirical quantitative and qualitative models.
References
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609.
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.
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.
Blöchlinger, A., & Leippold, M. (2006). Economic benefit of powerful credit scoring. Journal of Banking and Finance, 30(3), 851–873.
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.
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.
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.
Doumpos, M., & Pasiouras, F. (2005). Developing and testing models for replicating credit ratings: A multicriteria approach. Computational Economics, 25(4), 327–341.
Doumpos, M., & Zopounidis, C. (2011). A multicriteria ouranking modeling approach for credit rating. Decision Sciences, 42(3), 721–742.
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.
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.
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.
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.
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.
Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109.
Oliver, R. M. (2013). Financial performance measures in credit scoring. EURO Journal on Decision Processes, 1(3–4), 169–185.
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.
Piramuthu, S. (1999). Financial credit-risk evaluation with neural and neurofuzzy systems. European Journal of Operational Research, 112(2), 310–321.
Srinivasan, V., & Kim, Y. H. (1987). Credit granting: A comparative analysis of classification procedures. The Journal of Finance, 42(3), 665–681.
Tabouratzi, E., Lemonakis, C., & Garefalakis, A. (2017). Determinants of failure in greek manufacturing SMEs. Corporate Ownership and Control, 14(3), 45–50.
West, D. (2000). Neural network credit scoring models. Computers and Operations Research, 27(11–12), 1131–1152.
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.
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.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Doumpos, M., Lemonakis, C., Niklis, D., Zopounidis, C. (2019). Data Analytics for Developing and Validating Credit Models. In: Analytical Techniques in the Assessment of Credit Risk. EURO Advanced Tutorials on Operational Research. Springer, Cham. https://doi.org/10.1007/978-3-319-99411-6_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-99411-6_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99410-9
Online ISBN: 978-3-319-99411-6
eBook Packages: Business and ManagementBusiness and Management (R0)