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Artificial intelligence: accelerator or panacea for financial crime?

Author

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  • Peter Yeoh

Abstract

Purpose - This purpose of this viewpoint is to address the intended good and unintended bad impacts of artificial intelligence (AI) applications in financial crime. Design/methodology/approach - The paper relied primarily on secondary data resources, business cases and relevant laws and regulations, and it used a legal-economics perspective. Findings - Current AI systems could function as antidotes or accelerator of financial crime, in particular cybercrime. Research suggests criminal law could be applied via three approaches to curb these cybercrimes. However, others considered this to be an inappropriate mechanism to hold AI agents accountable, as present AI systems were not deemed capable of making ethically informed choices. Instead, administrative sanctions would be considered more appropriate for now. While keeping vigilance against AI malicious acts, regulatory authorities in the USA and the UK have opted largely for the innovation-friendly, market-oriented, permissionless approach over the state-interventionist stance so as to maintain their global competitive edge in this domain. Originality/value - The paper reinforced the growing arguments that AI applications should be deployed more as panacea for financial crimes rather than being abused as crime accelerators. There equally though is the need for both public and private sectors to be mindful of the unintended negative, harmful consequences to society, especially those connected to cybercrime. This implied the further need to beef up attention and resources to help mitigate these risks.

Suggested Citation

  • Peter Yeoh, 2019. "Artificial intelligence: accelerator or panacea for financial crime?," Journal of Financial Crime, Emerald Group Publishing Limited, vol. 26(2), pages 634-646, April.
  • Handle: RePEc:eme:jfcpps:jfc-08-2018-0077
    DOI: 10.1108/JFC-08-2018-0077
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    Citations

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    Cited by:

    1. Igor Vuletic, 2023. "Corporate Criminal Liability: An Overview of the Croatian Model after 20 Years of Practice," Laws, MDPI, vol. 12(2), pages 1-11, March.
    2. Karim, Sitara & Shafiullah, Muhammad & Naeem, Muhammad Abubakr, 2024. "When one domino falls, others follow: A machine learning analysis of extreme risk spillovers in developed stock markets," International Review of Financial Analysis, Elsevier, vol. 93(C).

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