Financial Risk Forecasting Using Naїve and Tree Augmented Classifier Based on Bayesian Networks

Олександр Миколайович Терентьєв, Вікторія Едуардівна Кириченко, Наталія Олександрівна Связінська, Тетяна Іванівна Просянкіна-Жарова

Abstract


Background. Development and study of characteristics for naïve and tree-augmented classifiers in the form of Bayesian networks in the problem of credit risk estimation.

Objective. To perform estimation of classification quality for the bank credit borrowers using Bayesian classifiers of two types.

Methods. Development of necessary mathematical tools and performing computational experiments aiming towards constructing classifiers in the form of Bayesian networks using actual statistical data characterizing solvency of bank credit borrowers.

Results. The following results were achieved: the methodology of constructing and application of the naïve and tree-augmented Bayesian classifiers for solving the problem of solvency estimation for bank credit borrowers; an analysis of computational algorithmic complexity was performed; two classification models were constructed in the form of Bayesian networks using actual statistical data from banking system; a comparative analysis was performed for the models developed.

Conclusions. It was established that the tree-augmented classifier exhibits higher computational complexity than the naïve Bayesian one, but it showed higher classification results while solving the problem of bank clients classification into two groups: those who return the credits and those who don’t.


Keywords


Intellectual data analysis; Bayesian networks; Credit scoring; Financial analysis; Macroeconomic indicators

References


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DOI: http://dx.doi.org/10.20535/1810-0546.2016.2.63882

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