IDEAS home Printed from https://ideas.repec.org/a/pal/jbkreg/v25y2024i4d10.1057_s41261-024-00233-2.html
   My bibliography  Save this article

Legal implications of automated suspicious transaction monitoring: enhancing integrity of AI

Author

Listed:
  • Umut Turksen

    (Coventry University)

  • Vladlena Benson

    (Aston University)

  • Bogdan Adamyk

    (Aston University)

Abstract

The fast-paced advances of technology, including artificial intelligence (AI) and machine learning (ML), continue to create new opportunities for banks and other financial institutions. This study reveals the barriers to trust in AI by prudential banking supervisors (compliance with regulations). We conducted a qualitative study on the drivers for adoption of explainability technologies that increase transparency and understanding of complex algorithms (some of the underpinning legal principles in the proposed EU AI Act). By using human-centred and ethics-by-design methods coupled with interviews of the key stakeholders from Eastern European private and public banks and IT AI/ML developers, this research has identified the key challenges concerning the employment of AI algorithms. The results indicate a conflicting view of AI barriers whilst revealing the importance of AI/ML systems in banks, the growing willingness of banks to use such systems more widely, and the problematic aspects of implementing AI/ML systems related to their cost and economic efficiency. Keeping up with the complex regulation requirements comes at a significant cost to banks and financial firms. The focus of the empirical study, stakeholders in Ukraine, Estonia and Poland, was chosen because of the fact that there has been a sharp increase in the adoption of AI/ML models in this jurisdiction in the context of its war with Russia and the ensuing sanctions regime. While the “leapfrogging” AI/ML paths in each bank surveyed had its own drivers and challenges, these insights provide lessons for banks in other European jurisdictions. The analysis of four criminal cases brought against top banks and conclusions of the study indicate that the increase in predicate crimes for money laundering, constantly evolving sanctions regime along with the enhanced scrutiny and enforcement action against banks are hindering technology innovation and legal implications of using AI driven tools for compliance.

Suggested Citation

  • Umut Turksen & Vladlena Benson & Bogdan Adamyk, 2024. "Legal implications of automated suspicious transaction monitoring: enhancing integrity of AI," Journal of Banking Regulation, Palgrave Macmillan, vol. 25(4), pages 359-377, December.
    • Handle: RePEc:pal:jbkreg:v:25:y:2024:i:4:d:10.1057_s41261-024-00233-2
    DOI: 10.1057/s41261-024-00233-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41261-024-00233-2
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/s41261-024-00233-2?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Barry Eichengreen, 2023. "Financial regulation in the age of the platform economy," Journal of Banking Regulation, Palgrave Macmillan, vol. 24(1), pages 40-50, March.
    2. Jiafu An & Raghavendra Rau, 2021. "Finance, technology and disruption," The European Journal of Finance, Taylor & Francis Journals, vol. 27(4-5), pages 334-345, March.
    3. Vladlena Benson & Umut Turksen & Bogdan Adamyk, 2023. "Dark side of decentralised finance: a call for enhanced AML regulation based on use cases of illicit activities," Journal of Financial Regulation and Compliance, Emerald Group Publishing Limited, vol. 32(1), pages 80-97, December.
    4. Christian Janiesch & Patrick Zschech & Kai Heinrich, 2021. "Machine learning and deep learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 685-695, September.
    5. Pedro Guerra & Mauro Castelli, 2021. "Machine Learning Applied to Banking Supervision a Literature Review," Risks, MDPI, vol. 9(7), pages 1-24, July.
    6. Bonnie G Buchanan & Danika Wright, 2021. "The impact of machine learning on UK financial services," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 37(3), pages 537-563.
    7. Rui Wang & Jiangtao Liu & Hang(Robin) Luo, 2021. "Fintech development and bank risk taking in China," The European Journal of Finance, Taylor & Francis Journals, vol. 27(4-5), pages 397-418, March.
    8. Collins, Christopher & Dennehy, Denis & Conboy, Kieran & Mikalef, Patrick, 2021. "Artificial intelligence in information systems research: A systematic literature review and research agenda," International Journal of Information Management, Elsevier, vol. 60(C).
    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. Patrick Weber & K. Valerie Carl & Oliver Hinz, 2024. "Applications of Explainable Artificial Intelligence in Finance—a systematic review of Finance, Information Systems, and Computer Science literature," Management Review Quarterly, Springer, vol. 74(2), pages 867-907, June.
    2. Serhan Cevik, 2024. "The dark side of the moon? Fintech and financial stability," International Review of Economics, Springer;Happiness Economics and Interpersonal Relations (HEIRS), vol. 71(2), pages 421-433, June.
    3. Zhao, Yang & Goodell, John W. & Wang, Yong & Abedin, Mohammad Zoynul, 2023. "Fintech, macroprudential policies and bank risk: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 87(C).
    4. Niklas Kühl & Max Schemmer & Marc Goutier & Gerhard Satzger, 2022. "Artificial intelligence and machine learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2235-2244, December.
    5. Jen-Yu Lee & Tien-Thinh Nguyen & Hong-Giang Nguyen & Jen-Yao Lee, 2022. "Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe," Energies, MDPI, vol. 15(11), pages 1-15, May.
    6. Mostafa Bigdeli & Mahsa Akbari, 2024. "Machine-learning-based Classification of Customers’ Behavioural Model in Instagram," Paradigm, , vol. 28(2), pages 223-240, December.
    7. Eduard Hartwich & Alexander Rieger & Johannes Sedlmeir & Dominik Jurek & Gilbert Fridgen, 2023. "Machine economies," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-13, December.
    8. Najla Alharbi & Bashayer Alkalifah & Ghaida Alqarawi & Murad A. Rassam, 2024. "Countering Social Media Cybercrime Using Deep Learning: Instagram Fake Accounts Detection," Future Internet, MDPI, vol. 16(10), pages 1-22, October.
    9. Kyriazis, Nikolaos & Corbet, Shaen, 2024. "Evaluating the dynamic connectedness of financial assets and bank indices during black-swan events: A Quantile-VAR approach," Energy Economics, Elsevier, vol. 131(C).
    10. Rui Ma & Jia Wang & Wei Zhao & Hongjie Guo & Dongnan Dai & Yuliang Yun & Li Li & Fengqi Hao & Jinqiang Bai & Dexin Ma, 2022. "Identification of Maize Seed Varieties Using MobileNetV2 with Improved Attention Mechanism CBAM," Agriculture, MDPI, vol. 13(1), pages 1-16, December.
    11. Roberto Cascante-Yarlequé & Purificación Galindo-Villardón & Fabricio Guevara-Viejó & José Luis Vicente-Villardón & Purificación Vicente-Galindo, 2025. "HJ-BIPLOT : A Theoretical and Empirical Systematic Review of Its 38 Years of History, Using Text Mining and LLMs," Mathematics, MDPI, vol. 13(12), pages 1-35, June.
    12. Dylan Norbert Gono & Herlina Napitupulu & Firdaniza, 2023. "Silver Price Forecasting Using Extreme Gradient Boosting (XGBoost) Method," Mathematics, MDPI, vol. 11(18), pages 1-15, September.
    13. Fatima Zohra BENBRAHMI & Hadj M’hammed MEHOR, 2025. "The Impact Of Artificial Intelligence On Enhancing The Travel Experience For Tourists: An Analysis Of Successful Models," Business Excellence and Management, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 15(5), pages 5-13, May.
    14. Cheng Yang & Fuhao Sun & Yujie Zou & Zhipeng Lv & Liang Xue & Chao Jiang & Shuangyu Liu & Bochao Zhao & Haoyang Cui, 2024. "A Survey of Photovoltaic Panel Overlay and Fault Detection Methods," Energies, MDPI, vol. 17(4), pages 1-37, February.
    15. Fang, Yi & Wang, Qi & Wang, Fan & Zhao, Yang, 2023. "Bank fintech, liquidity creation, and risk-taking: Evidence from China," Economic Modelling, Elsevier, vol. 127(C).
    16. Tianlei Pi & Haoxuan Hu & Jingyi Lu & Xue Chen, 2022. "The Analysis of Fintech Risks in China: Based on Fuzzy Models," Mathematics, MDPI, vol. 10(9), pages 1-13, April.
    17. Hong, Jichao & Li, Kerui & Liang, Fengwei & Yang, Haixu & Zhang, Chi & Yang, Qianqian & Wang, Jiegang, 2024. "A novel state of health prediction method for battery system in real-world vehicles based on gated recurrent unit neural networks," Energy, Elsevier, vol. 289(C).
    18. Shuai Sang & Lu Li, 2024. "A Novel Variant of LSTM Stock Prediction Method Incorporating Attention Mechanism," Mathematics, MDPI, vol. 12(7), pages 1-20, March.
    19. Vladimir Franki & Darin Majnarić & Alfredo Višković, 2023. "A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector," Energies, MDPI, vol. 16(3), pages 1-35, January.
    20. Zhiyu Cao & Zihan Chen & Prerna Mishra & Hamed Amini & Zachary Feinstein, 2023. "Modeling Inverse Demand Function with Explainable Dual Neural Networks," Papers 2307.14322, arXiv.org, revised Oct 2023.

    More about this item

    Keywords

    Artificial intelligence; Machine learning; Trust; Explainability; Transparency; Suspicious transactions; Anti-money laundering; Banking;
    All these keywords.

    JEL classification:

    • 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
    • K23 - Law and Economics - - Regulation and Business Law - - - Regulated Industries and Administrative Law
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives

    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:pal:jbkreg:v:25:y:2024:i:4:d:10.1057_s41261-024-00233-2. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave-journals.com/ .

    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.