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Prospects of Artificial Intelligence and Machine Learning Application in Banking Risk Management

Author

Listed:
  • Nenad Milojević

    (Mirabank a.d. Belgrade, Republic of Serbia)

  • Srdjan Redzepagic

    (Université Côte d'Azur, Graduate School in Economics and Management, Nice, France)

Abstract
Artificial intelligence and machine learning have increasing influence on the financial sector, but also on economy as a whole. The impact of artificial intelligence and machine learning on banking risk management has become particularly interesting after the global financial crisis. The research focus is on artificial intelligence and machine learning potential for further banking risk management improvement. The paper seeks to explore the possibility for successful implementation yet taking into account challenges and problems which might occur as well as potential solutions. Artificial intelligence and machine learning have potential to support the mitigation measures for the contemporary global economic and financial challenges, including those caused by the COVID-19 crisis. The main focus in this paper is on credit risk management, but also on analysing artificial intelligence and machine learning application in other risk management areas. It is concluded that a measured and well-prepared further application of artificial intelligence, machine learning, deep learning and big data analytics can have further positive impact, especially on the following risk management areas: credit, market, liquidity, operational risk, and other related areas.

Suggested Citation

  • 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.
    • Handle: RePEc:cbk:journl:v:10:y:2021:i:3:p:41-57
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    File URL: http://www.cbcg.me/repec/cbk/journl/vol10no3-3.pdf
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    References listed on IDEAS

    as
    1. Nikola Fabris, 2020. "Financial Stability and Climate Change," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 9(3), pages 27-43.
    2. Iñaki Aldasoro & Leonardo Gambacorta & Paolo Giudici & Thomas Leach, 2023. "Operational and Cyber Risks in the Financial Sector," International Journal of Central Banking, International Journal of Central Banking, vol. 19(5), pages 340-402, December.
    3. 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.
    4. Anastasiadis Panagiotis & Katsaros Efthymios & Koutsioukis Anastasios-Taxiarchis & Pandazis Athanasios, 2020. "GARCH Modelling of High-Capitalization Cryptocurrencies' Impacts During Bearish Markets," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 9(3), pages 87-106.
    5. Dominique Guegan, 2018. "Credit Risk Analysis Using machine and Deep Learning Models," Post-Print halshs-01889154, HAL.
    6. 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".
    7. Nenad Milojević, 2014. "Optimal Banking and Other Financial Business for the Economic Growth of Serbia," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 3(2), pages 61-83.
    8. 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.
    9. 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.
    10. Adrian, Tobias, 2017. "Risk Management and Regulation," CEPR Discussion Papers 12422, C.E.P.R. Discussion Papers.
    11. Dominique Guegan & Peter Martey Addo & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Post-Print halshs-01835164, HAL.
    12. Dominique Guegan, 2018. "Credit Risk Analysis Using machine and Deep Learning Models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01889154, HAL.
    13. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep Learning models," Post-Print halshs-01719983, HAL.
    14. 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.
    15. Zoran Grubišić & Sandra Kamenković & Tijana Kaličanin, 2021. "Comparative Analysis of the Banking Sector Competitiveness in Serbia and Montenegro," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 10(1), pages 75-91.
    16. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    17. 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.
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    Cited by:

    1. 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.
    2. Diptes C. P. Bhimjee, 2022. "Adaptive Early Warning Systems: An Axiomatic Approach," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 11(2), pages 145-164.

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    More about this item

    Keywords

    Banking; Risk Management; Artificial Intelligence; Machine Learning; Deep Learning; Big Data Analytics.;
    All these keywords.

    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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