Abstract
There has been increasing interest in risk scoring and bankruptcy prediction in recent years. Most of the current proposals analyse a set of parameters to classify companies as either active or default. What banks really need, however, is to be able to predict the probability of bankruptcy occurring in the future. Current approaches do not enable a deeper analysis to estimate the direction of a company as the parameters under study evolve. This article proposes a system for the Bankruptcy Scenario Query (B-SQ) which is based on association rules to allow users to conduct “What if...?” queries, and obtain as a response what usually happens under similar scenarios with the corresponding probability of it occurring.
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
Similar content being viewed by others
Notes
- 1.
URL: https://www.axesor.com.
- 2.
Axesor assigns a code to each company in the database.
- 3.
URL: http://www.cnae.com.es/.
References
Azayite, F.Z., Achchab, S.: Hybrid discriminant neural networks for bankruptcy prediction and risk scoring. Procedia Comput. Sci. 83, 670–674 (2016)
Balcázar, J.L.: Redundancy, deduction schemes, and minimum-size bases for association rules. Log. Methods Comput. Sci. 6(2), 1–33 (2010)
Basel Committee on Banking Supervision2: Basel II: International Convergence of Capital Measurement and Capital Standards: A Revised Framework - Comprehensive Version (2006)
Fallahpour, S., Lakvan, E.N., Zadeh, M.H.: Using an ensemble classifier based on sequential floating forward selection for financial distress prediction problem. J. Retail. Consum. Serv. 34, 159–167 (2017)
Gepp, A., Kumar, K.: Predicting financial distress: a comparison of survival analysis and decision tree techniques. Procedia Comput. Sci. 54, 396–404 (2015)
Gordini, N.: A genetic algorithm approach for SMEs bankruptcy prediction: empirical evidence from Italy. Expert Syst. Appl. 41(14), 6433–6445 (2014)
Huang, Y.: Prediction of contractor default probability using structural models of credit risk: an empirical investigation. Constr. Manage. Econ. 27(6), 581–596 (2009)
Iturriaga, F.J.L., Sanz, I.P.: Bankruptcy visualization and prediction using neural networks: a study of U.S. commercial banks. Expert Syst. Appl. 42(6), 2857–2869 (2015)
du Jardin, P.: Dynamics of firm financial evolution and bankruptcy prediction. Expert Syst. Appl. 75, 25–43 (2017)
Lin, W.Y., Hu, Y.H., Tsai, C.F.: Machine learning in financial crisis prediction: a survey. IEEE Trans. Syst. Man Cybern. (C) 42(4), 421–436 (2012)
Marín, N., Molina, C., Serrano, J.M., Vila, A.: A complexity guided algorithm for association rule extraction on fuzzy datacubes. IEEE Tras. Fuzzy Syst. 16, 693–714 (2008)
Martin-Barragan, B., Lillo, R., Romo, J.: Interpretable support vector machines for functional data. Europ. J. Oper. Res. 232(1), 146–155 (2014)
Molina, C., Rodriguez-Ariza, L., Sanchez, D., Vila, A.: A new fuzzy multidimensional model. IEEE Trans. Fuzzy Syst. 14(6), 897–912 (2006)
Ng, S.T., Wong, J.M., Zhang, J.: Applying z-score model to distinguish insolvent construction companies in China. Habitat Int. 35(4), 599–607 (2011)
Olson, D.L., Delen, D., Meng, Y.: Comparative analysis of data mining methods for bankruptcy prediction. Decis. Support Syst. 52(2), 464–473 (2012)
Pal, R., Kupka, K., Aneja, A.P., Militky, J.: Business health characterization: a hybrid regression and support vector machine analysis. Expert Syst. Appl. 49, 48–59 (2016)
de Reyes, M.A.P., Molina, C., Yáñez, M.C.P.: Interpretable associations over datacubes: application to hospital managerial decision making. In: Conference Proceedings of MIE 2014 (2014)
Shortliffe, E., Buchanan, B.: A model of inexact reasoning in medicine. Math. Biosci. 23(3–4), 351–379 (1975)
Tsai, C.F., Hsu, Y.F., Yen, D.C.: A comparative study of classifier ensembles for bankruptcy prediction. Appl. Soft Comput. 24, 977–984 (2014)
Tserng, H.P., Liao, H.H., Jaselskis, E.J., Tsai, L.K., Chen, P.C.: Predicting construction contractor default with barrier option model. J. Constr. Eng. Manage. 138(5), 621–630 (2012)
Yeh, C.C., Chi, D.J., Hsu, M.F.: A hybrid approach of DEA, rough set and support vector machines for business failure prediction. Expert Syst. Appl. 37(2), 1535–1541 (2010)
Ziba, M., Tomczak, S.K., Tomczak, J.M.: Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction. Expert Syst. Appl. 58, 93–101 (2016)
Acknowledgements
This research is partially funded by Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (FEDER) under project TIN2014-58227-P Descripción lingüística de información visual mediante técnicas de minería de datos y computación flexible. and project TIC1582 Mejora de la Accesibilidad a la información mediante el uso de contextos e interprtaciones adaptadas al usuario of the Junta de Andalucia (Spain).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Molina, C., Prados-Suárez, B., Cortes-Romero, A. (2017). Bankruptcy Scenario Query: B-SQ. In: Moral, S., Pivert, O., Sánchez, D., Marín, N. (eds) Scalable Uncertainty Management. SUM 2017. Lecture Notes in Computer Science(), vol 10564. Springer, Cham. https://doi.org/10.1007/978-3-319-67582-4_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-67582-4_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67581-7
Online ISBN: 978-3-319-67582-4
eBook Packages: Computer ScienceComputer Science (R0)