Artificial Intelligence in Anti Money Laundering Processes in Swiss Banks

With the accelerated digitalization due to COVID-19, sophisticated money laundering practices as well as Switzerland’s global position as a global financial center, Swiss Banks need a tool to be efficient and effective in complying to the regulators and moreover, in doing whatever is right for wide society – combating  the financial crime. For this the banks need sound Anti Money Laundering tools. Artificial Intelligence  plays a role in enhancing the tools the banks may have.

The bachelor's thesis provides an interdisciplinary dive into the Artificial Intelligence on the one hand and Anti Money Laundering  on the other, subsequently examining the Artificial Intelligence supported Anti Money Laundering tools. The thesis reviews the newest literature on the topic and names the use cases. Then validates the foundings with the qualitative research. Six expert interviews are conducted on the topic and they are transcribed, coded and analysed in MAXQDA using the content analysis method. Thus, the thesis answers two research questions: What are the contemporary use cases of Artificial Intelligence use in Anti Money Laundering processes? And what are the motivators and challenges for the Swiss banks on this way?

The study illuminates the significant use-cases of Artificial Intelligence within Anti Money Laundering. These use-cases include Screening, Onboarding, Detection, Monitoring, and Risk Profiling. Artificial Intelligence's potential to enhance these Anti Money Laundering processes, despite differing adoption levels, is acknowledged. Secondly, the research identifies drivers and obstacles for the Swiss banks adopting Artificial Intelligence in Anti Money Laundering processes. Effectiveness and efficiency surfaced as primary motivators some other important drivers are also identified. However, integrating Artificial Intelligence into Anti Money Laundering processes is not without challenges. Operational obstacles such as data quality and volume pose significant concerns, with data pooling being a less favorable solution in the Swiss context. Another notable challenge lies in the need for Artificial Intelligence explainability, an essential aspect for FINMA, the Swiss Financial Market Supervisory Authority.

The study acknowledges its limitations such as: the sample size, potential restraint in questions and responses due to the sensitive topic, and a degree of subjectivity due to the author being the sole evaluator and content analyst. These limitations, however, pave the way for further exploration in this critical field of Artificial Intelligence in Anti Money Laundering within the Swiss banking sector.

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