  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.
