Estimation of credit risks using the data mining methods

Authors

DOI:

https://doi.org/10.20535/SRIT.2308-8893.2017.1.03

Keywords:

credit risk, statistical data, logit model, Bayesian networks, model quality parameters

Abstract

In this research, credit risks are analyzed for financial organizations using data mining techniques applied to actual data. The two sets of actual statistical data characterizing the borrowers are employed for constructing mathematical models in the form of the nonlinear logit regression, decision trees, and Bayesian networks. The constructed models are analyzed with a set of appropriate statistical criteria, providing a basis for selecting the best alternative model. A series of computational experiments have been carried out using the two sets of actual statistical data from a Ukrainian bank. As a result of the performed computations, it was established that the best models in this application turned out to be nonlinear logit equations and Bayesian networks. In the future studies, we suppose to expand the number of model constructing techniques and to apply the idea of combining the estimates generated by the alternative models. Also, a specialized decision support system is to be constructed for the purpose of carrying research in the area of financial risks estimation and prediction.

Author Biographies

Valery Ya. Danylov, The Institute for Applied System Analysis at the Igor Sikorsky Kyiv Polytechnic Institute, Kyiv

Valery Danilov,

Doctor of Engineering Sciences, professor at the Institute for Applied System Analysis NTUU "KPI". Graduated from the Taras Shevchenko Kyiv National University in 1972, department of cybernetics. He got his PhD (Candidate of Physical and Mathematical Sciences) degree in Control Engineering in 1979, and Doctor of Engineering Sci. in 1993.

Current areas of interest: system analysis, identification and control of dynamic systems with chaos, fractal analysis, and artificial intelligence.

Alex L. Jirov, The department of Modeling Economic Systems at the NTUU "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv

Alex Jirov,

candidate of engineering sciences, associate professor at the department of Modeling Economic Systems at the NTUU "KPI". Graduated from the Taras Shevchenko Kyiv National University in 1977, department of cybernetics. He got his PhD (Candidate of Sciences) degree in Control Engineering in 1993.

Current areas of interest: system analysis, guarantying estimation of systems in condition of uncertainty.

Petro I. Bidyuk, The Institute for Applied System Analysis at the Igor Sikorsky Kyiv Polytechnic Institute, Kyiv

Petro Bidyuk,

Doctor of Engineering Sciences, professor at the Institute for Applied System Analysis NTUU "KPI". Graduated from the Kyiv Polytechnic Institute in 1972, department of electronics. He got his PhD (Candidate of Sciences) degree in Control Engineering in 1986, and Doctor of Engineering Sci. in 1996.

Current areas of interest: time series analysis, forecasting and control of dynamic systems, Bayesian data analysis, and decision support systems (design and implementation).

References

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Published

2017-03-21

Issue

Section

Decision making and control in economic, technical, ecological and social systems