IMPROVING THE QUALITY OF CREDIT ACTIVITY BY USING SCORING MODEL

Authors

  • K. V. Melnyk National Technical University “Kharkiv Polytechnic Institute”, Kharkiv, Ukraine
  • N. V. Borysova National Technical University “Kharkiv Polytechnic Institute”, Kharkiv, Ukraine

DOI:

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

Keywords:

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

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.

Author Biographies

K. V. Melnyk, National Technical University “Kharkiv Polytechnic Institute”, Kharkiv

PhD, Associate Professor of the Department of Software Engineering and Management Information
Technologies

N. V. Borysova, National Technical University “Kharkiv Polytechnic Institute”, Kharkiv

PhD, Associate professor of the Department of Intelligent Computer Systems

References

Bank Lending Survey [Electronic resource] / Access mode:https://bank.gov.ua/control/en/publish/category;jsessionid=4

AEC639346EC2333E34BA6A8D94B48CA?cat_id=20741795, 03.12.2018.

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

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.

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.

Kevin Johnston. How to Evaluate a Firm’s Credit Worthiness [Electronic resource], Access mode:

https://smallbusiness.chron.com/evaluate-firms-creditworthiness- 25925.html, 30.11.2018.

Celan Bryant. How to Determine The Creditworthiness of a Customer [Electronic resource], Access mode:

https://blog.apruve.com/how-to-determine-thecreditworthiness- of-a-customer. – 03.12.2018.

Motwani A., Chaurasiya P., Bajaj G. Predicting Credit Worthiness of Bank Customer with Machine Learning Over Cloud, International journal of computer sciences and engineering, 2018, No. 6(7), pp. 1471–1477. DOI:10.26438/ijcse/v6i7.14711477

Shvidkij A. I., Miroshnichenko A. A. Metody ocenki kreditosposobnosti korporativnyx klientov kommercheskogobanka: rossijskij i zarubezhnyj opyt, Mezhdunarodnyj zhurnalprikladnyx i fundamental’nyx issledovanij, 2016, No. 7–4, pp. 667–672.

Gotovchikov I. F. Prakticheskij metod e’kspress-ocenki finansovyx vozmozhnostej fizicheskix i yuridicheskix lic,

Bankovskoe kreditovanie, 2009, No. 3, P. 115.

Pramod S. Pati, Aghav Dr. J. V., Sareen Vikram An Overview of Classification Algorithms and Ensemble Methods in

Personal Credit Scoring, International Journal of Computer Science and technology, 2016, Vol. 7, Issue 2, pp. 183–188.

Thabiso Peter Mpofu, Mukosera Macdonald Credit Scoring Techniques: A Survey, International Journal of Computer

Science and technology, 2014, Vol. 3, Issue 8, pp. 165–168.

Ukrainian bureau credit history [Electronic resource] Access mode: https://ubki.ua/ua . 05.12.2018.

Mobile app “Credit history” [Electronic resource] Access mode:https://play.google.com/store/apps/details?id=ua.ubki

&hl=uk . 05.12.2018.

Internet-bank Privat24 [Electronic resource], Access mode: https://www.privat24.ua/ . 10.12.2018.

Eibe Frank, Mark A. Hall, Christopher J. Palestro and Ian H. Witten Data Mining: Practical Machine Learning Tools and

Techniques, Elsevier Science & Technology Books, 2016, 654 p.

Kesavaraj G., Sukumaran S. A study on classification techniques in data mining, Fourth International Conference on

Computing, Communications and Networking Technologies, Tiruchengode, 2013, pp. 1–7. DOI:10.1109/ICCCNT.2013.6726842

Mel’nik K. V., Єrshova S. І. Problemy i osnovnye podxody k resheniyu zadachi medicinskoj diagnostiki, Sistemi

obrobki іnformacії, 2011, No. 2 (92), pp. 244–248.

Mariya Yao, Adelyn Zhou, Marlene Jia Applied Artificial Intelligence: A Handbook For Business Leaders Paperback,

Topbots Inc, 2018, 228 p.

Ian Goodfellow, Yoshua Bengio, Aaron Courville. Deep Learning, MIT Press, 2016, 800 p.

Naive Bayes Classifiers [Electronic resource], Access mode:https://www.geeksforgeeks.org/naive-bayes-classifiers/,04.12.2018.

Mel’nik K. V., Glushko V. N. Primenenie apparata Bajesovyx setej pri obrabotke dannyx iz medicinskix kartochek, Science and Education a New Dimension: Natural and Technical Sciences, 2013, I (2), Issue 15, pp. 126–129. Vengriya, Budapesht.

Mel’nik K. V., Goloskokov A. E. Ispol’zovanie setej doveriya dlya zadachi skrininga, Tezi dopovіdej mіzhnarodnoїnaukovo-praktichnoї konferencії «Іnformacіjnі texnologії: nauka, texnіka, texnologіya, osvіta, zdorov’ya». Xarkіv, NTU «XPІ», 2014, P. 14.

Google Analytics Referral Spam [Electronic resource], Access mode:https://medium.com/@lenguyenthedat/google-analyticsreferral-spam-85bb6b7aed2b, 02.11.2018.

Koji Miyahara, Michael J. Pazzani Improvement of Collaborative Filtering with the Simple Bayesian Classifier [Electronic

resource], University of California, Irvine. Access mode:https://www.ics.uci.edu/~pazzani/Publications/IPSJ.pdf. 01.12.2018.

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.

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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Published

2019-05-28

How to Cite

Melnyk, K. V., & Borysova, N. V. (2019). IMPROVING THE QUALITY OF CREDIT ACTIVITY BY USING SCORING MODEL. Radio Electronics, Computer Science, Control, (2), 60–70. https://doi.org/10.15588/1607-3274-2019-2-7

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Section

Mathematical and computer modelling