Soft-Hard Data Fusion Using Uncertainty Balance Principle – Evidence from Corporate Credit Risk Assessment in Commercial Banking

Sabina Brkić, Migdat Hodzic, Enis Džanić

Abstract


This study introduces Uncertainty Balance Principle (UBP) as a new concept/method for incorporating additional soft data into probabilistic credit risk assessment models. It shows that soft banking data, used for credit risk assessment, can be expressed and decomposed using UBP and thus enabling more uncertainty to be handled with a precise mathematical methodology. The results show that this approach has relevance to credit risk assessment models in the sense that it proved its usefulness for the purpose of soft-hard data fusion, it modified Probability of Default with soft data modeled using possibilistic (fuzzy) distributions and fused with hard probabilistic data via UBP and it obtained better classification prediction results on the overall sample. This was demonstrated on a simple example of one soft variable, two experts and a small sample and thus this is an approach/method that requires further research, enhancements and rigorous statistical testing for the application to a complete scoring and/or rating system

Keywords


Soft Data Hard Data Credit Default Uncertainty Balance Principle Expert Opinion Data Fusion

Full Text:

PDF

References


Agarwal, P., & Najal, H.S. (2015). Possibility theory vs possibility theory in fuzzy measure theory, Int. Journal of Engineering Research and Applications 5(5), 37–43.

Babashamsi, P., Golzadfar, A., Yusoff, N.I., Ceylan, H., & Nor, N.G. (2016). Integrated fuzzy analytic hierarchy process and VIKOR method in the prioritization of pavement maintenance activities. Int. J. Pavement Res. Technol., 9(2), 112-120.

Bank for International Settlements (February, 2005a), Working Paper No. 15: Studies on the Validation of Internal Rating Systems, Basel: Basel Committee on Banking Supervision, Available at: www.bis.org.

Bank for International Settlements (July, 2005b), An Explanatory Note on the Basel II IRB Risk Weight Functions, Basel: Basel Committee on Banking Supervision, Available at: www.bis.org.

Bellman, R., & Zadeh, L. (1979). Decision-making in a fuzzy environment. Management science.

Bennett, J.C., Bohoris, G.A., Aspinwall, E.M., & Hall, R.C. (1996). Risk analysis techniques and their application to software development. European Journal of Operational Research, 95(3), 467-475.

Blochwitz, S., Hamerle, A., Hohl, S., Rauhmeier & R., Rösch, D. (2005). Myth and reality of discriminatory power for rating systems. Wilmott Magazine, pp. 2-6.

Brkić, S., Hodžić, M., & Džanić, E. (2017). Fuzzy Logic Model of Soft Data Analysis for Corporate Client Credit Risk Assessment in Commercial Banking. Fifth Scientific Conference with International Participation “Economy of Integration” ICEI 2017, Available at SSRN:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3079471

Brkić, S., Hodžić, M., & Džanić, E. (2019). Soft Data Modeling via Type 2 Fuzzy Distributions for Corporate Credit Risk Assessment in Commercial Banking, In Avdakovic, S. (Ed) Advanced Technologies, Systems and Applications III (pp. 457-469). Springer, Cham.

Central Bank of Bosnia and Herzegovina (2017). The Financial stability report 2017. Retrieved October 10, 2018, Available at: www.cbbh.ba

Coolen, F.P.A., et al. (2010). Imprecise probability. In: Lovric M. (eds) International Encyclopedia of Statistical Science. Berlin, Heidelberg: Springer.

de Cooman, G. (1996). Possibility theory 1, the measure- and integral-theoretic groundwork. Universiteit Gent, Vakgroep Elektrische Energietechniek.

Dubois, D. (2006). Possibility theory and statistical reasoning. Institut de Recherche en Informatique de Toulouse.

Dubois, D., & Prade, H. (1983). Unfair coins and necessity measures: towards a possibilistic interpretation of histograms. Fuzzy Sets Syst., 10(1), 15–20.

Dubois, D., & Prade, H. (1986). Fuzzy sets and statistical data. Eur. J. Oper. Res., 25(3), 345–356.

Dubois, D., & Prade, H. (1987). The mean value of a fuzzy number. Fuzzy Sets Syst., 24(3), 279–300.

Dubois, D., & Prade, H. (1992). When upper probabilities are possibility measures. Fuzzy Sets Syst., 49(1), 65–74.

Dubois, D., & Prade, H. (2002). Possibility theory probability theory and multiple valued logics: a clarification. Annal. Math. Artif. Intell., 32, 35–66.

Dubois, D., & Prade, H., (1988). Possibility Theory. New York: Plenum.

Dubois, D., Prade, H. & Smets, P. (2001). New semantics for quantitative possibility theory. 2nd International Sympoium on Imprecise Probabilities and Their Applications (pp. 152-161). Ithaca, New York.

Engelmann, B., Hayden, E., & Tasche, D. (2003). Testing for Rating Accuracy, Risk 16, January, 82-86.

Eschenbach, W. (2012). Triangular Fuzzy Numbers and the IPCC. Retrieved October 19, 2018, from https://wattsupwiththat.com/2012/02/07/triangular-fuzzy-numbers-and-the-ipcc/

Feller, W. (1950). An Introduction to Probability Theory and Its Applications. New York: Willey.

Garibaldi, J.M., & John. R.I., (2003). Choosing Membership Functions of Linguistic Terms. Proceedings of the 2003 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2003), (pp. 578-583). St. Louis, USA.

Hair Jr. J., Black W. C., Babin B. J. & Andersen R. E. (2010). Multivariate data analysis, 7th Edition, Prentice Hall.

Hayden E. (2011). Estimation of a Rating Model for Corporate Exposures. In Engelmann, B., & Rauhmeier, R, (eds) The Basel II Risk Paramenters (pp.13-24). London: Springer.

Hayden E., Porath D., (2011). Statistical Methods to Develop Rating Models. In Engelmann, B., & Rauhmeier, R, (eds) The Basel II Risk Paramenters (pp.1-12). London: Springer.

Hodžić, M. (2016a). Fuzzy to Random Uncertainty Alignment. Southeast Europe Journal of Soft Computing, 5(1), 58-66.

Hodzic, M. (2016b). Uncertainty Balance Principle. IUS Periodicals of engineering and natural sciences, 4(2), 17-32.

Hodzic, M. (2018). Soft to Hard Data Transformation Using Uncertainty Balance Principle. In Hadzikadic, M. & Avdakovic, S. (Eds) Advanced Technologies, Systems and Applications II (pp. 785-809). Springer International Publishing.

Hodzic, M. (2019). A Platform for Human-Machine Information Data Fusion, In Avdakovic, S. (Ed) Advanced Technologies, Systems and Applications III (pp. 430-456). Springer, Cham.

Hosmer, D., W., & Lemeshow, S. (2010). Applied Logistic Regrresion, 2nd Ed. NJ, USA. John Wiley & Sons, Inc.

Iancu, I., Mamdani, A. (2012). Type fuzzy logic controller. In Dadios, E. (ed.) Fuzzy logic—controls, concepts, theories and applications (pp. 325-350). InTechOpen.

Jenkins, M.P., et al. (2015). Towards context aware data fusion: modeling and integration of situationally qualified human observations to manage uncertainty in a hard-soft fusion process. Information Fusion, 21(1), 130–144.

Kaufmann, A., & Gupta, M.M. (1985). Introduction to Fuzzy Arithmetic, Theory and Applications. New York: Reinhold, Van Nost.

Kaur, B., Bala, M., & Kumar, M. (2014). Comparitive analysis of fuzzy based wildfire detection techniques. Int. J. Sci. Eng. Res., 5(7), 813-818.

Leon-Garcia, A. (2008). Probability, statistics, and random processes for electrical engineers (3rd ed). Upper Saddle River, NJ: Pearson Prentice Hall - Pearson Education, Inc.

Liu, B. (2012). Why is there a need for uncertainty theory? J. Uncertain. Syst., 6(1), 3–10.

Mauris, G. (2011). Possibility distributions: a unified representation of usual direct-probability-based parameter estimation methods. Int. J. Approx. Reason., 52, 1232–1242.

Narukawa, Y., Torra, V., & Gakuen, T. (2016). Fuzzy measure and probability distributions: distorted probabilities. Retrieved July 20, 2018 from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.183.2292&rep=rep1&type=pdf

Onuwa, O.B. (2014). Fuzzy expert system for malaria diagnosis. Orient. J. Comput. Sci. Technol., 7 (2), 273–284.

Raufaste, E., & Neves, R.D.S. (1998). Empirical evaluation of possibility theory in human radiological diagnosis. In Prade, H. (ed.) 13th European Conference on Artificial Intelligence. Wiley.

Sanchez, L., Casillas, J., Cord, O., & Jose del Jesus, M. (2002). Some relationships between fuzzy and random set-based classifiers and models. Int. J. Approx. Reason., 29, 175–213.

Şentürk, S. (2010). Fuzzy regression control chart based on a-cut approximation. Int. J. Comput.Intell. Syst., 3(1), 123–140.

Shang, K., & Hossen, Z. (2013). Applying Fuzzy Logic to Risk Assessment and Decision-Making. Casualty Actuarial Society, Canadian Institute of Actuaries, Society of Actuaries, 2, 209-218.

Shapiro, A.F. (2009). Fuzzy random variables. Insur. Math. Econ., 44, 307–314.

van der Helm, R. (2008). Towards a clarification of probability, possibility and plausibility: How semantics could help futures practice to improve. Foresight, 8(3), 17–27.

Yang, M.S., & Liu, M.C. (1998). On possibility analysis of fuzzy data. Fuzzy Sets Syst., 94, 171–183.

Zadeh, L.A. (1965). Fuzzy sets. Inf. Control, 8(3), 338–353.

Zadeh, L.A. (2008). Is there a need for fuzzy logic? Info. Sci., 178(13), 2751–2779.

Zimmermann, H. J. (2001). Fuzzy Set Theory – and Its Applications (4th Edition). New York, Kluwer Academic Publishers.




DOI: http://dx.doi.org/10.21533/pen.v7i3.436

Refbacks

  • There are currently no refbacks.


Copyright (c) 2019 Migdat Hodzic, Sabina Brkić, Enis Džanić

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

ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

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