Model of Assessing the Overdue Debts in a Commercial Bank Using Neuro-Fuzzy Technologies

Dmytro Kovalenko, Olga Afanasieva, Nani Zabuta, Tetiana Boiko and Rosen Rosenov Baltov
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Dmytro Kovalenko: Department of Finance and Financial and Economic Security, Kyiv National University of Technologies and Design, Nemyrovycha-Danchenka Street, 2, 01011 Kyiv, Ukraine
Olga Afanasieva: Department «Management of the Hi-Tech Enterprises», Moscow Aviation Institute, National Research University, Volokolamskoe Shosse, 4, 125993 Moscow, Russia
Nani Zabuta: Department of Finance and Accounting, Open International University of Human Development “UKRAINE”, 23 Lvovskaya Street, 01001 Kiev, Ukraine
Tetiana Boiko: Department of Finance, Accounting and Marketing, Prydniprovska State Academy of Civil Engineering and Architecture, 24 a, Chernyshevsky Street, 49600 Dnepro, Ukraine
Rosen Rosenov Baltov: Bulgarian Academy of Sciences, 1 “15 Noemvri” Str., 1040 Sofia, Bulgaria

JRFM, 2021, vol. 14, issue 5, 1-20

Abstract: This article considers the problems of overdue credit debt and the creation of effective methods to manage problem debts in banks. The purpose of this paper is to study the problem of overdue credit debt and create effective methods to manage problem debts in financial institutions. Based on a combination of tools of fuzzy logic theory and artificial neural networks, an economic-mathematical model of collection scoring was built. Kohonen self-organizing maps were used to set the parameters of membership functions in the process of fuzzification of quantitative variables of the built model. Data were taken from the official websites of four Bulgarian banks for 2015–2019. The volume of the prepared sample amounted to 1000 credit agreements with active overdue payments. The practical value of the built model of collection scoring for the recovery of overdue debt lies in the possibility to make recommendations for work with each segment of the portfolio of overdue loans in accordance with the calculated level of credit risk. The introduction of credit risk assessment models based on neuro-fuzzy technologies in the work of financial institutions will have a positive impact on the financial results of lending activities of banks.

Keywords: collections scoring; credit risk; financial stability; cluster; neuro-even technology (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2021
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