Machine Learning in Transaction Monitoring: The Prospect of xAI

Julie Gerlings, Ioanna Constantiou

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Abstract

Banks hold a societal responsibility and regulatory requirements to mitigate the risk of financial crimes. Risk mitigation primarily happens through monitoring customer activity through Transaction Monitoring (TM). Recently, Machine Learning (ML) has been proposed to identify suspicious customer behavior, which raises complex socio-technical implications around trust and explainability of ML models and their outputs. However, little research is available due to its sensitivity. We aim to fill this gap by presenting empirical research exploring how ML supported automation and augmentation affects the TM process and stakeholders’ requirements for building eXplainable Artificial Intelligence (xAI). Our study finds that xAI requirements depend on the liable party in the TM process which changes depending on augmentation or automation of TM. Context-relatable explanations can provide much-needed support for auditing and may diminish bias in the investigator’s judgement. These results suggest a use case-specific approach for xAI to adequately foster the adoption of ML in TM.
OriginalsprogEngelsk
TitelProceedings of the 56th Hawaii International Conference on System Sciences
RedaktørerTung X. Bui
Antal sider10
UdgivelsesstedHonolulu
ForlagHawaii International Conference on System Sciences (HICSS)
Publikationsdato2023
Sider3474-3483
ISBN (Elektronisk)9780998133164
DOI
StatusUdgivet - 2023
BegivenhedThe 56th Hawaii International Conference on System Sciences. HICSS 2023 - Lahaina, USA
Varighed: 3 jan. 20236 jan. 2023
Konferencens nummer: 56
https://hicss.hawaii.edu/

Konference

KonferenceThe 56th Hawaii International Conference on System Sciences. HICSS 2023
Nummer56
Land/OmrådeUSA
ByLahaina
Periode03/01/202306/01/2023
Internetadresse
NavnProceedings of the Annual Hawaii International Conference on System Sciences
ISSN1060-3425

Emneord

  • High stakes decisions
  • AML (Anti-Money Laundering)
  • Decision-making
  • Machine learning
  • Explainable AI
  • xAI
  • Automation
  • Augmentation

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