Financial performance as a decision criterion of credit scoring models selection [doi: 10.21529/RECADM.2017004]

Rodrigo Alves Silva, Anderson Ara, Evandro Marcos Saidel Ribeiro
DOI: https://doi.org/10.21529/RECADM.2017004

Abstract

This paper aims to show the importance of the use of financial metrics in decision-making of credit scoring models selection. In order to achieve such, we considered an automatic approval system approach and we carried out a performance analysis of the financial metrics on the theoretical portfolios generated by seven credit scoring models based on main statistical learning techniques. The models were estimated on German Credit dataset and the results were analyzed based on four metrics: total accuracy, error cost, risk adjusted return on capital and Sharpe index. The results show that total accuracy, widely used as a criterion for selecting credit scoring models, is unable to select the most profitable model for the company, indicating the need to incorporate financial metrics into the credit scoring model selection process.


Keywords

Credit risk; Model’s selection; Statistical learning

Compartilhe

References


Ala'Raj, M., & Abbod, M. F. (2016). Classifiers consensus system approach for credit scoring. Knowledge-Based Systems, 104(15), 89–105.

Bache, K., & Lichman, M. (2013). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.

Bluhm, C., Overbeck, L. & Wagner, C. (2010). Introduction to credit risk modeling (2ªed.). London: Chapman & Hall, 386p.

Brown, I., & Mues, C. (2012). An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Systems with Applications, 39, 3446–3453.

Chung, H. Y., Ho, C. H., & Hsu, C. C. (2011). Support vector machines using Bayesian-based approach in the issue of unbalanced classifications. Expert Systems with Applications, 38(9), 11447–11452.

Duan, J. C., & Shrestha, K. (2011). Statistical Credit Rating Metholds. Global Credit Review, 1, 43-64.

Febraban, Relatório Anual. (2013). Recuperado em 05 de dezembro de 2014, de www.febraban.org.br/.

Ferson, W. E. (2013). Investment Performance: A Review and Synthesis. In. Handbook of the Economics of Finance, 2, part B.

García, F., Giménez, V., & Guijarro, F. (2013). Credit risk management: A multicriteria approach to assess creditworthiness. Mathematical and Computer Modelling, (57), 2009-2015.

Glantz, M., & Kissell, R. (2014). Multi-asset risk modeling: techniques for a global economy in an electronic and algorithmic trading era. San Diego: Academic Press.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis. (7ª ed.) New Jersey. Pearson Prentice Hall.

Harris, T. (2015). Credit scoring using the clustered support vector machine. Expert Systems with Applications, 42(2), 741–750.

Iscoe, I., Kreinin, A., Mausser, H., & Romanko, O. (2012). Portfolio credit-risk optimization. Journal of Banking & Finance, 36(6), 1604–1615.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: with application in R. New York: Springer.

Louzada, F., & Ara, A. (2012). Bagging k-dependence probabilistic networks: An alternative powerful fraud detection tool. Expert Systems with Applications, 39, 11583–11592.

Louzada, F., Ferreira-Silva, P. H., & Diniz, C. A. R. (2012). On the impact of disproportional samples in credit scoring models: An application to a Brazilian bank data. Expert Systems with Applications, 39(9), 8071–8078.

Louzada, F., Ara, A., & Fernandes, G. B. (In Press). Classification methods applied to credit scoring: Systematic review and overall comparison. Surveys in Operations Research and Management Science, (Previous screen in November/2016).

Lu, F. Q., Huang, M.; Ching, W. K., & Siu, T. K. (2013). Credit portfolio management using two-level particle swarm optimization. Information Sciences, 237(10), 162-175.

Nagarajan, R., Scutari, M., & Lèbre, S. (2013). Bayesian Networks in R: with Applications in Systems Biology. Nova York: Springer.

Pascual, M. B., Martínez, A. M., & Alamillos, A. M. (2014). Redes bayesianas aplicadas a problemas de credit scoring. Una aplicación práctica. Cuadernos de Economía, 104(37), 73-86.

Xiao, H., Xiao, Z., & Wang, Y. (2016). Ensemble classification based on supervised clustering for credit scoring. Applied Soft Computing, 43, 73–86.

Zhou, L., Lai, K. K., & Yu, L. (2010). Least squares support vector machines ensemble models for credit scoring. Expert Systems with Applications, 37(1), 127-133.




Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.