Machine Learning in Transaction Monitoring: The Prospect of xAI
This addresses the problem of adopting ML in sensitive financial crime detection for banks, but it is incremental as it builds on existing socio-technical concerns without introducing new methods.
The paper tackles the challenge of trust and explainability in using machine learning for transaction monitoring in banks, finding that explainable AI requirements vary based on whether the process is automated or augmented and that context-relatable explanations can aid auditing and reduce bias.
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.