IDEAS home Printed from https://ideas.repec.org/a/bkr/journl/v81y2022i3p107-127.html
   My bibliography  Save this article

A Method for Assessing the IT Component of Model Risk and the Economic Capital to Cover It

Author

Listed:
  • Evgeny Moiseev

    (Sberbank)

  • Denis Zagorodnev

    (Sberbank)

  • Alexander Berezinskiy

    (Sberbank)

  • Roman Tikhonov

    (Sberbank)

Abstract

This paper considers the problem of assessing the information technology component of model risk (ITMR) and the amount of capital allocated to compensate for it. We develop a methodology to identify inconsistencies between the environments for the development and application of the model being implemented, taking into account risk factors such as errors made when writing the programme code of the model to operate in an industrial environment, poor data quality, and the inappropriate choice of system for the implementation of the model and/or the data source systems for its application. We propose a method for estimating the cost of the realisation of the ITMR assessment for a business organisation (the assessment is conducted on a model-by-model basis) and a method for calculating the economic capital to cover this risk. The method proposed may be used to control ITMR by analysing the amount of losses from its realisation, the probability of such realisation, and the cost of measures to reduce model risk.

Suggested Citation

  • Evgeny Moiseev & Denis Zagorodnev & Alexander Berezinskiy & Roman Tikhonov, 2022. "A Method for Assessing the IT Component of Model Risk and the Economic Capital to Cover It," Russian Journal of Money and Finance, Bank of Russia, vol. 81(3), pages 107-127, September.
  • Handle: RePEc:bkr:journl:v:81:y:2022:i:3:p:107-127
    as

    Download full text from publisher

    File URL: https://rjmf.econs.online/upload/iblock/d3d/A-Method-for-Assessing-the-IT-Component-of-Model-Risk-and-the-Economic-Capital-to-Cover-It.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. van Liebergen, Bart, 2017. "Machine learning: A revolution in risk management and compliance?," Journal of Financial Transformation, Capco Institute, vol. 45, pages 60-67.
    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. John R. J. Thompson & Longlong Feng & R. Mark Reesor & Chuck Grace, 2021. "Know Your Clients’ Behaviours: A Cluster Analysis of Financial Transactions," JRFM, MDPI, vol. 14(2), pages 1-29, January.
    2. Sebastian Doerr & Jon Frost & Leonardo Gambacorta & Vatsala Shreeti, 2023. "Big techs in finance," BIS Working Papers 1129, Bank for International Settlements.
    3. Paolo Vanini & Sebastiano Rossi & Ermin Zvizdic & Thomas Domenig, 2023. "Online payment fraud: from anomaly detection to risk management," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-25, December.
    4. Stijn Claessens & Jon Frost & Grant Turner & Feng Zhu, 2018. "Fintech credit markets around the world: size, drivers and policy issues," BIS Quarterly Review, Bank for International Settlements, September.
    5. 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.
    6. Małgorzata Zaleska & Edyta Cegielska & Emil Ślązak, 2020. "Employment in the banking sector in Poland – determinants and perception," Bank i Kredyt, Narodowy Bank Polski, vol. 51(6), pages 661-686.
    7. Anil Savio Kavuri & Alistair Milne, 2019. "FinTech and the future of financial services: What are the research gaps?," CAMA Working Papers 2019-18, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    8. Michael Becker & Kevin Merz & Rüdiger Buchkremer, 2020. "RegTech—the application of modern information technology in regulatory affairs: areas of interest in research and practice," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(4), pages 161-167, October.
    9. Jon Frost & Leonardo Gambacorta & Yi Huang & Hyun Song Shin & Pablo Zbinden, 2019. "BigTech and the changing structure of financial intermediation," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 34(100), pages 761-799.
    10. Adam Bouland & Wim van Dam & Hamed Joorati & Iordanis Kerenidis & Anupam Prakash, 2020. "Prospects and challenges of quantum finance," Papers 2011.06492, arXiv.org.
    11. Cosma, Simona & Rimo, Giuseppe & Torluccio, Giuseppe, 2023. "Knowledge mapping of model risk in banking," International Review of Financial Analysis, Elsevier, vol. 89(C).
    12. Wall, Larry D., 2018. "Some financial regulatory implications of artificial intelligence," Journal of Economics and Business, Elsevier, vol. 100(C), pages 55-63.
    13. Flavio Bazzana & Marco Bee & Ahmed Almustfa Hussin Adam Khatir, 2024. "Machine learning techniques for default prediction: an application to small Italian companies," Risk Management, Palgrave Macmillan, vol. 26(1), pages 1-23, February.
    14. Michael Becker & Rüdiger Buchkremer, 2018. "Ranking of current information technologies by risk and regulatory compliance officers at financial institutions – a German perspective," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 10(1), pages 007-026, June.

    More about this item

    Keywords

    model risk; IT component; industrial environment; implementation; machine learning; model; model quality; data quality; risk factors; evaluation methodology; validation check;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

    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:bkr:journl:v:81:y:2022:i:3:p:107-127. 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: Olga Kuvshinova (email available below). General contact details of provider: https://edirc.repec.org/data/cbrgvru.html .

    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.