Bankrobotics: Artificial Intelligence and Machine Learning Powered Banking Risk Management: Prevention of money laundering and terrrorism financing
Alexandra Prisznyak ()
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Alexandra Prisznyak: PhD Candidate, Senior Consultant, Bankrobotics, CBDC Program Manager University of Pécs, Institute for Training and Consulting in Banking
Public Finance Quarterly, 2022, vol. 67, issue 2, 288 - 303
Based on a country study related to money laundering and terrorist financing, the Financial Action Group downgraded Hungary's compliance with Recommendation R15 (use of new technologies). At the same time, between 2020 and 2021, the Magyar Nemzeti Bank imposed fines on several commercial banks operating in Hungary for shortcomings on complying with money laundering and terrorist financing regulations. As a gap-filling analysis, the study examines supervised (classification, regression), unsupervised (clustering, anomaly detection), and hybrid machine learning models and algorithms operating based on highly unbalanced dataset of anti-money laundering and terrorism financing prevention of banking risk management. The author emphasizes that there is no one ideal algorithm. The choice between machine learning algorithm is highly determined based on the underlying theoretical logic and additional comparative. Model building requires a hybrid perspective of the give business unit, IT and visionary management.
Keywords: Artificial Intelligence; Machine Learning algorithms; banking risk management; Anti-Money Laundering and Counter Financing Terrorism; supervised/unsupervised methods (search for similar items in EconPapers)
JEL-codes: C45 C80 G21 G32 O33 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:pfq:journl:v:67:y:2022:i:2:p:288-303
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