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All issues Volume 91 (2021) SHS Web Conf., 91 (2021) 01019 References
Open Access
Issue
SHS Web Conf.
Volume 91, 2021
Innovative Economic Symposium 2020 – Stable Development in Unstable World (IES2020)
Article Number 01019
Number of page(s) 12
Section Stable Development in Unstable World
DOI https://doi.org/10.1051/shsconf/20219101019
Published online 14 January 2021
  1. M. Krejnina, Financial management. Moscow: Business and service (2002). [Google Scholar]
  2. Klyachkin V.N., Shunina Yu.S., System for Borrowers’ Creditworthiness Assessment and Repayment of Loans Forecasting, Vestnik komp'yuternykh i informatsionnykh tekhnologii, 11, p. 45-51 (2015), Available at: http://ezpro.fa.ru:2070/full_record.do?product=RSCI&search_mode=GeneralSearch&qid=1&SID=E1pr5h7xBiIjA2egM5z&page=1&doc=10&cacheurlFromRightClick=no. [Google Scholar]
  3. Francisco Louzada, Paulo H. Ferreira-Silva, Carlos A.R. Deniz (2012) On the impact of disproportional samples in credit scoring models: An application to a Brazilian bank data // Expert Systems with Applications, 39(9,3). Available at: http://www.sciencedirect.com/science/article/pii/S0957417412001522. [Google Scholar]
  4. M. Bakanov, A. Sheremet, Theory of economic analysis. 5th ed. Moscow: Finances and statistics (2002). [Google Scholar]
  5. E. Solozhencev, N. Stepanova, V. Karasev. Transparency of credit risks and ratings evaluation methods. St. Petersburg: St. Petersburg University Publishing House (2006). [Google Scholar]
  6. I. Vishnyakov. Methods and models of assessing the creditworthiness of borrowers. St. Petersburg: SPbSUEE (1998). [Google Scholar]
  7. V. Starshinov, System realization of credit scoring as a professional system to assess clients’ solvency. Information technologies in science, management, social sphere and medicine. -V Intenational science conference (2018). [Google Scholar]
  8. Vernikov, A. Mamonov, M., Modelling technical efficiency of firms under one-step and two-step approaches (the case of commercial banks), Applied Econometrics, 1, p. 67-90 (2018), Available at: http://ezpro.fa.ru:2070/full_record.do?product=RSCI&search_mode=GeneralSearch&qid=25&SID=E1pr5h7xBiIjA2egM5z&page=1&doc=8&cacheurlFromRightClick=no. [Google Scholar]
  9. O. Vorobyeva, Scoring model of creditworthiness evaluation. Moscow: Aktion Management and Finance (2020). [Google Scholar]
  10. I. Vorobyeva, Improving the Methodology of Credit Scoring of Borrowers in Car Lending, Den'gi i kredit, 8, p. 34-39 (2013), Available at: http://ezpro.fa.ru:2070/full_record.do?product=RSCI&search_mode=GeneralSearch&qid=25&SID=E1pr5h7xBiIjA2egM5z&page=2&doc=16. [Google Scholar]
  11. Panyagometh, K., Credit scoring by incorporating dynamic networked information, Economics & sociology, 1, p. 262-269 (2019), Available at: http://ezpro.fa.ru:2070/InterService.do?fromPID=RSCI&product=UA&toPID=UA&returnLink=http%3a%2f%2fapps.webofknowledge.com%2ffull_record.do%3fhighlighted_tab%3dRSCI%26last_prod%3dRSCI%26excludeEventConfig%3dExcludeIfFromFullRecPage. [Google Scholar]
  12. V. Selyukov, Optimization of Credit Risk Management in Banking Scoring Systems, Herald of the Bauman Moscow State Technical University. Series: Natural sciences, 1, p. 106-118 (2012), Available at: http://ezpro.fa.ru:2070/full_record.do?product=RSCI&search_mode=GeneralSearch&qid=31&SID=E1pr5h7xBiIjA2egM5z&page=4&doc=36. [Google Scholar]
  13. A. Masyutin, Credit scoring based on social network data, National Research University Higher School of Economics, Business Informatics (2015), 3, p. 15-23, Available at: http://ezpro.fa.ru:2070/full_record.do?product=RSCI&search_mode=GeneralSearch&qid=1&SID=E1pr5h7xBiIjA2egM5z&page=1&doc=9&cacheurlFromRightClick=no. [Google Scholar]
  14. Y. Zhosan, Methods of selective data combination in terms of credit scoring limitations. Moscow: Moscow State University Publishing House (2017). [Google Scholar]
  15. A. Sorokin, Creation of scoring schemes with the use of logisctic regression models. Vestnik evrazijskoj nauki, 2 (21) (2014). [Google Scholar]
  16. N.V. Grin', Methodological aspects of scoring models. Available at: https://docplayer.ru/35162944-Metodologicheskie-aspekty-postroeniya-skoringovyh-modeley-n-v-grin-grodnenskiy-gosudarstvennyy-universitet-imeni-yanki-kupaly.html (2012). [Google Scholar]
  17. G. Andreeva, Scoring as a method of credit risk evaluation. Bank technologies, № 6 (2000). [Google Scholar]
  18. Li Yibei, Wang Ximei, Credit scoring by incorporating dynamic networked information, European journal of operational research, 3, p. 1103-1112 (2020), Available at: http://ezpro.fa.ru:2070/InterService.do?fromPID=RSCI&product=UA&toPID=UA&returnLink=http%3a%2f%2fapps.webofknowledge.com. [Google Scholar]
  19. O. Zakharova, Development of a model of an information system for managing a lending process and an algorithm for cross-selling to bank borrowers, Software systems and computational methods, 1, p. 51-58 (2019), Available at: http://ezpro.fa.ru:2070/full_record.do?product=RSCI&search_mode=GeneralSearch&qid=31&SID=E1pr5h7xBiIjA2egM5z&page=1&doc=4&cacheurlFromRightClick=no. [Google Scholar]
  20. V. Salin, Statistical analysis of a banking system of Russia, Vestnik Finansovogo universiteta, 6, p. 107-115 (2015), Available at: http://ezpro.fa.ru:2070/full_record.do?product=RSCI&search_mode=GeneralSearch&qid=31&SID=E1pr5h7xBiIjA2egM5z&page=2&doc=13&cacheurlFromRightClick=no. [Google Scholar]
  21. V. Aleshin, Credit scoring is a tool to improve the quality of bank risk management in contemporary conditions, Terra Economicus, 10 (2-3), p. 27-30 (2012), Available at: http://ezpro.fa.ru:2070/full_record.do?product=RSCI&search_mode=GeneralSearch&qid=31&SID=E1pr5h7xBiIjA2egM5z&page=3&doc=28&cacheurlFromRightClick=no. [Google Scholar]
  22. Christian Bluhm, Ludger Overbeck, Christoph Wagner. «Introduction to Credit Risk Modeling», second edition, USA: Chapman and Hall/ CRC Financial Mathematics Series, 2010, 383 p. [Google Scholar]

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