IDEAS home Printed from https://ideas.repec.org/a/ssi/jouesi/v7y2020i4p3158-3172.html
   My bibliography  Save this article

Transparency of credit institutions

Author

Listed:
  • Roman P. Bulyga

    (Financial University under the Government of the Russian Federation, Russian Federation)

  • Alexey A. Sitnov

    (Financial University under the Government of the Russian Federation, Russian Federation)

  • Liudmila V. Kashirskaya

    (Financial University under the Government of the Russian Federation, Russian Federation)

  • Irina V. Safonova

    (Financial University under the Government of the Russian Federation, Russian Federation)

Abstract

The article is devoted to a problem of information transparency of credit institutions, which plays a key role in ensuring an effective interaction of such institutions with their stakeholders and is considered as one of the factors of their competitiveness and investment attractiveness. Credit organizations are the main players on the services’ market. Therefore, it is necessary to provide complete and most transparent information about the activities of a credit institution, in order to receive a high level of trust from both - clients and investors. This article contains an analysis of the stages of development of international regulatory rules in the specified subject area, which allowed to determine and structurize the information to be disclosed by banks and other credit organizations in accordance with the requirements of international financial institutions. The research methodology was based on the main international regulations issued by the Basel Committee on Banking Supervision and governing the activities of credit organizations. During the research it was concluded that it is reasonable to use the XBRL technology adaptation algorithm developed by the authors as a tool to increase the transparency of credit institutions. The result of the study was the development of this algorithm. A study conducted by the authors revealed that, in the development of the developed concept, its provisions on the development of accounting and analytical tools to ensure information transparency of credit institutions, as well as improving the control system for the reliability of data generated, provided and published by credit organizations, are specified.

Suggested Citation

  • Roman P. Bulyga & Alexey A. Sitnov & Liudmila V. Kashirskaya & Irina V. Safonova, 2020. "Transparency of credit institutions," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 7(4), pages 3158-3172, June.
    • Handle: RePEc:ssi:jouesi:v:7:y:2020:i:4:p:3158-3172
    DOI: 10.9770/jesi.2020.7.4(38)
    as

    Download full text from publisher

    File URL: https://jssidoi.org/jesi/uploads/articles/28/Bulyga_Transparency_of_credit_institutions.pdf
    Download Restriction: no
    File URL: https://jssidoi.org/jesi/article/577
    Download Restriction: no
    File URL: https://libkey.io/10.9770/jesi.2020.7.4(38)?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Dominique Guegan & Bertrand Hassani, 2018. "Regulatory learning: How to supervise machine learning models? An application to credit scoring," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01835213, HAL.
    2. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep learning models," Working Papers 2018:08, Department of Economics, University of Venice "Ca' Foscari".
    3. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep Learning models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01719983, HAL.
    4. Nabamita Dutta & Deepraj Mukherjee, 2018. "Can financial development enhance transparency?," Economic Change and Restructuring, Springer, vol. 51(4), pages 279-302, November.
    5. Cristina Bodea & Raymond Hicks, 2018. "Sovereign credit ratings and central banks: Why do analysts pay attention to institutions?," Economics and Politics, Wiley Blackwell, vol. 30(3), pages 340-365, November.
    6. Dominique Guegan & Peter Martey Addo & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01835164, HAL.
    7. Dominique Guegan, 2018. "Credit Risk Analysis Using machine and Deep Learning Models," Post-Print halshs-01889154, HAL.
    8. Dominique Guegan & Peter Martey Addo & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Post-Print halshs-01835164, HAL.
    9. Dominique Guegan, 2018. "Credit Risk Analysis Using machine and Deep Learning Models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01889154, HAL.
    10. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep Learning models," Post-Print halshs-01719983, HAL.
    11. Peter Martey Addo & Dominique Guégan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep learning models," Documents de travail du Centre d'Economie de la Sorbonne 18003, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    12. Galina Petrova & Elena Posadneva & Tatiana Morozova, 2019. "Leading the Labour Market by the Laws of Supply and Demand," Springer Proceedings in Business and Economics, in: Wadim Strielkowski (ed.), Sustainable Leadership for Entrepreneurs and Academics, pages 263-271, Springer.
    13. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Risks, MDPI, vol. 6(2), pages 1-20, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dimitrios Nikolaidis & Michalis Doumpos, 2022. "Credit Scoring with Drift Adaptation Using Local Regions of Competence," SN Operations Research Forum, Springer, vol. 3(4), pages 1-28, December.
    2. Dan Wang & Zhi Chen & Ionut Florescu, 2021. "A Sparsity Algorithm with Applications to Corporate Credit Rating," Papers 2107.10306, arXiv.org.
    3. Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.
    4. Gunnarsson, Björn Rafn & vanden Broucke, Seppe & Baesens, Bart & Óskarsdóttir, María & Lemahieu, Wilfried, 2021. "Deep learning for credit scoring: Do or don’t?," European Journal of Operational Research, Elsevier, vol. 295(1), pages 292-305.
    5. Roy Cerqueti & Francesca Pampurini & Annagiulia Pezzola & Anna Grazia Quaranta, 2022. "Dangerous liasons and hot customers for banks," Review of Quantitative Finance and Accounting, Springer, vol. 59(1), pages 65-89, July.
    6. Theuri, Joseph & Olukuru, John, 2022. "The impact of Artficial Intelligence and how it is shaping banking," KBA Centre for Research on Financial Markets and Policy Working Paper Series 61, Kenya Bankers Association (KBA).
    7. José Américo Pereira Antunes, 2021. "To supervise or to self-supervise: a machine learning based comparison on credit supervision," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-21, December.
    8. Keerthana Sivamayil & Elakkiya Rajasekar & Belqasem Aljafari & Srete Nikolovski & Subramaniyaswamy Vairavasundaram & Indragandhi Vairavasundaram, 2023. "A Systematic Study on Reinforcement Learning Based Applications," Energies, MDPI, vol. 16(3), pages 1-23, February.
    9. Amirhosein Mosavi & Yaser Faghan & Pedram Ghamisi & Puhong Duan & Sina Faizollahzadeh Ardabili & Ely Salwana & Shahab S. Band, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Mathematics, MDPI, vol. 8(10), pages 1-42, September.
    10. Salima Smiti & Makram Soui, 2020. "Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTE," Information Systems Frontiers, Springer, vol. 22(5), pages 1067-1083, October.
    11. Anastasios Petropoulos & Vasilis Siakoulis & Evaggelos Stavroulakis & Aristotelis Klamargias, 2019. "A robust machine learning approach for credit risk analysis of large loan level datasets using deep learning and extreme gradient boosting," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Are post-crisis statistical initiatives completed?, volume 49, Bank for International Settlements.
    12. Anastasios Petropoulos & Vasilis Siakoulis & Evaggelos Stavroulakis & Aristotelis Klamargias, 2019. "A robust machine learning approach for credit risk analysis of large loan-level datasets using deep learning and extreme gradient boosting," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
    13. Seyyide Doğan & Yasin Büyükkör & Murat Atan, 2022. "A comparative study of corporate credit ratings prediction with machine learning," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 32(1), pages 25-47.
    14. Martin Leo & Suneel Sharma & K. Maddulety, 2019. "Machine Learning in Banking Risk Management: A Literature Review," Risks, MDPI, vol. 7(1), pages 1-22, March.
    15. Nenad Milojević & Srdjan Redzepagic, 2021. "Prospects of Artificial Intelligence and Machine Learning Application in Banking Risk Management," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 10(3), pages 41-57.
    16. Hossein Hassani & Xu Huang & Emmanuel Silva & Mansi Ghodsi, 2020. "Deep Learning and Implementations in Banking," Annals of Data Science, Springer, vol. 7(3), pages 433-446, September.
    17. Irving Fisher Committee, 2019. "The use of big data analytics and artificial intelligence in central banking," IFC Bulletins, Bank for International Settlements, number 50, July.
    18. Kim, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2020. "Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting," European Journal of Operational Research, Elsevier, vol. 283(1), pages 217-234.
    19. A. R. Provenzano & D. Trifir`o & A. Datteo & L. Giada & N. Jean & A. Riciputi & G. Le Pera & M. Spadaccino & L. Massaron & C. Nordio, 2020. "Machine Learning approach for Credit Scoring," Papers 2008.01687, arXiv.org.
    20. Yaseen Ghulam & Kamini Dhruva & Sana Naseem & Sophie Hill, 2018. "The Interaction of Borrower and Loan Characteristics in Predicting Risks of Subprime Automobile Loans," Risks, MDPI, vol. 6(3), pages 1-21, September.

    More about this item

    Keywords

    information disclosure; information openness; information transparency; transparency; credit institution; bank; stakeholders;
    All these keywords.

    JEL classification:

    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting
    • M42 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Auditing
    • M49 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Other
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ssi:jouesi:v:7:y:2020:i:4:p:3158-3172. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Manuela Tvaronaviciene (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.