Machine Learning in Transaction Monitoring: The Prospect of xAI

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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.
Original languageEnglish
Title of host publicationProceedings of the 56th Hawaii International Conference on System Sciences
EditorsTung X. Bui
Number of pages10
Place of PublicationHonolulu
PublisherHawaii International Conference on System Sciences (HICSS)
Publication date2023
ISBN (Electronic)9780998133164
Publication statusPublished - 2023
EventThe 56th Hawaii International Conference on System Sciences. HICSS 2023 - Lahaina, United States
Duration: 3 Jan 20236 Jan 2023
Conference number: 56


ConferenceThe 56th Hawaii International Conference on System Sciences. HICSS 2023
Country/TerritoryUnited States
Internet address
SeriesProceedings of the Annual Hawaii International Conference on System Sciences


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

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