DOI: https://doi.org/10.15588/1607-3274-2019-2-7

IMPROVING THE QUALITY OF CREDIT ACTIVITY BY USING SCORING MODEL

K. V. Melnyk, N. V. Borysova

Abstract


Context. The problem of credit assessment of a client is considered. It is a simultaneous processing of lender’s data of different
nature with further definition of the credit rating. The object of this study was the process of lending to individuals by credit institutions.
Objective. The purpose of the work is to study the process of improving the quality of lending through the development and use
of a scorecard model.
Method. An analytical review of the domain area was conducted. A business process model for assessing clients’ creditworthiness
in the form of an IDEF0 diagram is developed. Dedicated groups of indicators characterizing a potential lender from different
directions. Selected sets of values for each indicator of credit separately. The methods of solving the problem of clients’ creditworthiness
are analyzed. Selected Bayesian naive classifier as a method for solving the problem of classification of potential lenders. The
existing information systems for assessing the creditworthiness of clients are analyzed. A scoring model for assessing credit ratings
by the client in the form of an algorithm is developed. The list of functional requirements of the information system, which is presented
in the form of a use case diagram is determined. Three-level architecture for the information system is proposed. A database
model has been developed to preserve customer information. An information system was developed for determining the credit rating
of a client based on the developed scoring model. Numerous studies have been conducted to determine the class of a potential creditor.
The process of determining the quality of credit activity is analyzed. Quality indicators for assessing the creditworthiness of clients
are selected. The method of calculating the quality of credit activity is offered.
Results. The scoring model was developed, which was used in solving the credit assessment of clients through the help of the
proposed information system. The process of improving the quality of credit rating is investigated.
Conclusions. The conducted experiments have confirmed the proposed scoring model and allow recommending it for use in
practice for assessment process of client creditworthiness. Scientific novelty is to improve the process of credit activity by automating
the use of naïve Bayes classifier, which reduces the human factor in decision-making.

Keywords


scoring model, classification task, naive Bayesian classifier, credit score assessment, lending, lender, borrower, and creditworthiness.

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References


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AEC639346EC2333E34BA6A8D94B48CA?cat_id=20741795, 03.12.2018.
2. Carlson Mark, Shan Hui, Warusawitharana Missaka Capital ratios and bank lending: A matched bank approach, Journal
of Financial Intermediation, 2013, Volume 22, Issue 4, pp. 663–687. https://doi.org/10.1016/j.jfi.2013.06.003
3. Ling Kock Sheng, Teh Ying Wah A comparative study of data mining techniques in predicting consumers’ credit card
risk in banks, African Journal of Business Management, 2011, Vol. 5 (20), pp. 8307–8312.
4. Polozhennya pro kredituvannya, zatv. Postanovoyu Pravlіnnya NBU 28.09.1995 № 246, Pravove regulyuvannya
kreditnix vіdnosin v Ukraїnі: 36 normat. Aktіv, Kiev, Yurіnkom Іnter, 2001, pp. 53–66.
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https://blog.apruve.com/how-to-determine-thecreditworthiness-of-a-customer. – 03.12.2018.
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obrobki іnformacії, 2011, No. 2 (92), pp. 244–248.
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naukovo-praktichnoї konferencії «Іnformacіjnі texnologії: nauka, texnіka, texnologіya, osvіta, zdorov’ya». Xarkіv, NTU «XPІ», 2014, P. 14.
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resource], University of California, Irvine. Access mode:https://www.ics.uci.edu/~pazzani/Publications/IPSJ.pdf. 01.12.2018.
25. Mel’nik K. V. Ocіnka yakostі medichnoї іnformacії, Materіali Mіzhnarodnoї naukovo-praktichnoї konferencії “Problemi і perspektivi rozvitku ІT іndustrії”. Xar’kov, XNEU іmenі Semena Kuznecya, 2018, P. 70.
26. Woodall P. M., Oberhofer M., & Borek A. A Classification of Data Quality Assessment and Improvement Methods, International
Journal of Information Quality, 2014, No. 3. https://doi.org/10.1504/IJIQ.2014.068656.







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