Международная студенческая научно-практическая конференция «Инновационное развитие государства: проблемы и перспективы глазам молодых ученых». Том 2

Pyastolova A.A., Oglih V.V., Mudrenko A.A.

Oles Honchar Dnipropetrovsk National University, Ukraine


Crediting plays a crucial role in the banking market. Credit granting as a resource for the development of the modern economy is becoming more urgent. The crisis in the Ukrainian economy, an accretion of demand for banking services, an increase in delay in credit repayments, which share currently makes up about 30 per cent, all these factors can lead to so-called a credit risk rise. Credit risk is the risk of credit institution losses as a result of default, late or incomplete execution debtor's financial obligations to the credit institution in accordance with the terms of the contract [1].

The risk inherent in banking activities primarily depends on creditworthiness. The quality of loan portfolio is determined by the accuracy of a client’s assessment. Today, credit scoring is one of the most popular methods for solving this problem.

Scoring is a system for evaluating a borrower’s creditworthiness on the basis of expert estimates and numerical statistical methods. The use of these methods helps to determine the profitability of credit allowance to individuals and businesses and to assess the risk of default funds.

Scoring is a multifaceted concept, and therefore to increase the efficiency of its use it is necessary to distinguish its types according to the problems it solves:

¾ application scoring is assessment of client’s creditworthiness to obtain a credit;

¾ behavioral is assessment of the probability to a granted credit return;

¾ collection is evaluation of the possibility of full or partial repayment in the event of a credit term exceeding;

¾ response is evaluation of a consumer’s reaction to the proposal submitted to him;

¾ fraud is evaluation of the probability that a new client will not cheat;

¾ attrition is estimate of the probability of a further use of a product or move to another supplier.

Scoring is widely used in Western banking system. Complex and unstable economic and political situation in Ukraine determines the immaturity of the credit market. Nowadays, only one type of scoring is implemented in practice: application scoring. These two approaches are used to implement it:

1) Retrospective scoring is based on analysis of historical data. It is implemented using various mathematical methods and it helps to identify important fields in a borrower’s form.

2) Expert scoring is an assessment of a borrower, which determines his ability to pay, based on quantitative and qualitative data about a customer.

At present, the second approach to scoring estimate is more popular. In Ukraine, this process consists of the following stages:

1. The experts select a set of required information (fields of forms) which determines borrower’s creditworthiness, taking into account the current regulations of the NBU and data about the bank’s previous borrowers.

2. The specialists of Risk Management Division are developing a methodology to estimate a borrower’s financial performance and paying capacity by using scores for estimating client’s quantitative and quality indicators.

3. These data are registered into the program of calculation, which determines a client’s rate and category of debt service depending on the received score.

4. The bank makes a decision about the possibility and conditions of lending and the results of data processing are taken into account.

But this estimate is not optimally efficient, because it does not provide for the active use of historical data ignores the experience of the bank and has precise distinctions, which does not allow any deviations.

In our opinion, combining two methods of scoring is of great interest. Moreover, the active development of fuzzy logic device provides an opportunity to improve the scoring model and single out a new category of borrowers, besides solvent and insolvent ones. It is a middle link, which requires the preparation of additional agreements, raising an interest rate of credit, risk insurance, and so on in order to estimate a client more accurately.

So the issue of constructing an integrated model to assess borrower’s creditworthiness remains open.


1. Грушенков Р.В. Банковские риски. Неизвестная категория заемщиков / Р.В. Грушенков // Труды МГТА: электронный журнал. – 2011. – №19. – С.1.

2. Юринець Р.В. Економетрична модель оцінювання кредитного позичальника відповідно до експертної оцінки / Р.В. Юринець // Науковий вісник НЛТУ України : зб. наук.-техн. праць. – Львів : РВВ НЛТУ України. – 2009. – Вип. 19.5. – С. 255.