Applications to Corporate Default Prediction and Consumer Credit

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


This chapter illustrates the application of analytical predictive and descriptive techniques for credit risk assessment. To this end, two case applications are presented using data sets involving corporate defaults and credit card loans. The first part is devoted to the prediction of corporate defaults. A data set of 13,414 European small and medium-sized manufacturing enterprises (SMEs) from six countries is considered during the period 2009–2011. The information available for the firms involves their financial characteristics. Corporate default prediction models are constructed with statistical, machine learning, and multicriteria decision making techniques. The analysis of the results covers both the predictive performance of the models, as well as the insights that they provide regarding the factors that affect the default risk for European SMEs. In the second part, a descriptive multivariate clustering approach is employed to obtain analyze credit card loan applications. A publicly available data set of 30,000 cases is analyzed with the k-medoids algorithm to identify clusters of borrowers having similar characteristics. The results are discussed in terms of the common features of the clusters and their level of credit risk.


  1. Adams, N. M., Hand, D. J., & Till, R. J. (2001). Mining for classes and patterns in behavioural data. Journal of the Operational Research Society, 52(9), 1017–1024.CrossRefGoogle Scholar
  2. Akkoç, S. (2012). An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data. European Journal of Operational Research, 222(1), 168–178.CrossRefGoogle Scholar
  3. Altman, E. I., & Sabato, G. (2007). Modelling credit risk for SMEs: Evidence from the U.S. market. Abacus, 43(3), 332–357.CrossRefGoogle Scholar
  4. Alves, B. C., & Dias, J. G. (2015). Survival mixture models in behavioral scoring. Expert Systems with Applications, 42(8), 3902–3910.CrossRefGoogle Scholar
  5. Angilella, S., & Mazzù, S. (2015). The financing of innovative SMEs: A multicriteria credit rating model. European Journal of Operational Research, 244(2), 540–554.CrossRefGoogle Scholar
  6. Bijak, K., & Thomas, L. C. (2012). Does segmentation always improve model performance in credit scoring? Expert Systems with Applications, 39(3), 2433–2442.CrossRefGoogle Scholar
  7. Bravo, C., Thomas, L. C., & Weber, R. (2015). Improving credit scoring by differentiating defaulter behaviour. Journal of the Operational Research Society, 66(5), 771–781.CrossRefGoogle Scholar
  8. Chamboko, R., & Bravo, J. M. (2016). On the modelling of prognosis from delinquency to normal performance on retail consumer loans. Risk Management, 18(4), 264–287.CrossRefGoogle Scholar
  9. Crook, J. N., Edelman, D. B., & Thomas, L. C. (2007). Recent developments in consumer credit risk assessment. European Journal of Operational Research, 183(3), 1447–1465.CrossRefGoogle Scholar
  10. Dinh, T. H. T., & Kleimeier, S. (2007). A credit scoring model for Vietnam’s retail banking market. International Review of Financial Analysis, 16(5), 471–495.CrossRefGoogle Scholar
  11. Doumpos, M., Niklis, D., Zopounidis, C., & Andriosopoulos, K. (2015). Combining accounting data and a structural model for predicting credit ratings: Empirical evidence from European listed firms. Journal of Banking and Finance, 50, 599–607.CrossRefGoogle Scholar
  12. Emekter, R., Tu, Y., Jirasakuldech, B., & Lu, M. (2015). Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending. Applied Economics, 47(1), 54–70.CrossRefGoogle Scholar
  13. Garefalakis, A., Alexopoulos, G., Tsatsaronis, M., & Lemonakis, C. (2017). Financial and investment strategies to captivate S&P 500 volatility premium. Investment Management and Financial Innovations, 14(3), 39–53.CrossRefGoogle Scholar
  14. Hsieh, N. (2005). Hybrid mining approach in the design of credit scoring models. Expert Systems with Applications, 28(4), 655–665.CrossRefGoogle Scholar
  15. Jiang, C., Wang, Z., Wang, R., & Ding, Y. (2018). Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending. Annals of Operations Research, 266(1–2), 511–529.CrossRefGoogle Scholar
  16. Jones, S., Johnstone, D., & Wilson, R. (2015). An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes. Journal of Banking and Finance, 56, 72–85.CrossRefGoogle Scholar
  17. Kao, L.-J., & Lee, C.-F. (2012). Alternative method for determining industrial bond ratings: Theory and empirical evidence. International Journal of Information Technology and Decision Making, 11(6), 1215–1235.CrossRefGoogle Scholar
  18. Kvamme, H., Sellereite, N., Aas, K., & Sjursen, S. (2018). Predicting mortgage default using convolutional neural networks. Expert Systems with Applications, 102, 207–217.CrossRefGoogle Scholar
  19. Lemonakis, C., Voulgaris, F., Vassakis, K., & Christakis, S. (2015). Efficiency, capital and risk in banking industry: The case of Middle East and North Africa (MENA) countries. International Journal of Financial Engineering and Risk Management, 2(2), 109.CrossRefGoogle Scholar
  20. Li, K., Niskanen, J., Kolehmainen, M., & Niskanen, M. (2016). Financial innovation: Credit default hybrid model for SME lending. Expert Systems with Applications, 61, 343–355.CrossRefGoogle Scholar
  21. Livingston, M., Poon, W. P. H., & Zhou, L. (2018). Are Chinese credit ratings relevant? A study of the Chinese bond market and credit rating industry. Journal of Banking and Finance, 87, 216–232.CrossRefGoogle Scholar
  22. Luo, S.-T., Cheng, B.-W., & Hsieh, C.-H. (2009). Prediction model building with clustering-launched classification and support vector machines in credit scoring. Expert Systems with Applications, 36(4), 7562–7566.CrossRefGoogle Scholar
  23. Mizen, P., & Tsoukas, S. (2012). Forecasting US bond default ratings allowing for previous and initial state dependence in an ordered probit model. International Journal of Forecasting, 28(1), 273–287.CrossRefGoogle Scholar
  24. Nguyen, H.-T. (2015). Default predictors in credit scoring: Evidence from France’s retail banking institution. The Journal of Credit Risk, 11(2), 41–66.CrossRefGoogle Scholar
  25. Nikolic, N., Zarkic-Joksimovic, N., Stojanovski, D., & Joksimovic, I. (2013). The application of brute force logistic regression to corporate credit scoring models: Evidence from Serbian financial statements. Expert Systems with Applications, 40(15), 5932–5944.CrossRefGoogle Scholar
  26. Oliveira, M. D. N. T., Ferreira, F. A. F., Pérez-Bustamante Ilander, G. O., & Jalali, M. S. (2017). Integrating cognitive mapping and MCDA for bankruptcy prediction in small- and medium-sized enterprises. Journal of the Operational Research Society, 68(9), 985–997.CrossRefGoogle Scholar
  27. Sanchez-Barrios, L. J., Andreeva, G., & Ansell, J. (2016). Time-to-profit scorecards for revolving credit. European Journal of Operational Research, 249(2), 397–406.CrossRefGoogle Scholar
  28. Sarlija, N., Bensic, M., & Zekic-Susac, M. (2009). Comparison procedure of predicting the time to default in behavioural scoring. Expert Systems with Applications, 36(5), 8778–8788.CrossRefGoogle Scholar
  29. Serrano-Cinca, C., & Gutiérrez-Nieto, B. (2016). The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending. Decision Support Systems, 89, 113–122.CrossRefGoogle Scholar
  30. Sohn, S. Y., & Kim, Y. S. (2013). Behavioral credit scoring model for technology-based firms that considers uncertain financial ratios obtained from relationship banking. Small Business Economics, 41(4), 931–943.CrossRefGoogle Scholar
  31. Sun, J., Li, H., Huang, Q.-H., & He, K.-Y. (2014). Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowledge-Based Systems, 57, 41–56.CrossRefGoogle Scholar
  32. Szczerba, M., & Ciemski, A. (2009). Credit risk handling in telecommunication sector (pp. 117–130).CrossRefGoogle Scholar
  33. Van Gestel, T., Martens, D., Baesens, B., Feremans, D., Huysmans, J., & Vanthienen, J. (2007). Forecasting and analyzing insurance companies’ ratings. International Journal of Forecasting, 23(3), 513–529.CrossRefGoogle Scholar
  34. Van Gool, J., Verbeke, W., Sercu, P., & Baesens, B. (2012). Credit scoring for microfinance: Is it worth it? International Journal of Finance and Economics, 17(2), 103–123.CrossRefGoogle Scholar
  35. Vanneschi, L., Horn, D. M., Castelli, M., & Popovič, A. (2018). An artificial intelligence system for predicting customer default in e-commerce. Expert Systems with Applications, 104, 1–21.CrossRefGoogle Scholar
  36. Voulgaris, F., & Lemonakis, C. (2014). Competitiveness and profitability: The case of chemicals, pharmaceuticals and plastics. The Journal of Economic Asymmetries, 11, 46–57.CrossRefGoogle Scholar

Copyright information

© 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

Personalised recommendations