Financial institutions are required by their governments to detect and deter criminal activity, commonly known as Anti-Money Laundering (AML). AML professionals use a constellation of software tools to facilitate this work; The design of these tools and the way they fit together can impact the effectiveness of an investigation and the amount of effort required. We conducted 15 semi-structured interviews of AML professionals and analyzed their responses using reflexive thematic analysis. The results reveal a breadth of socio-technical challenges that financial institutions face in implementing an AML system. Many AML systems fail to address all aspects of an institution’s workflow and lack necessary data leading users to follow manual processes and workarounds. These shortcomings provide opportunity for the use of ML/AI— based technologies. These results inform design considerations for AML systems, and add financial institutions to the empirical literature around designing and deploying collaborative systems for enterprise settings.
ACM CHI Conference on Human Factors in Computing Systems