Automation of Generating Suspicious Transaction Reports to Singapore's Financial Intelligence Unit: the Use-case of FinTech Company
Name
Richard Kuklane
Abstract
Regulatory reporting is an increasingly important topic in the financial domain as a critical means to ensure transparency, accountability, and compliance with legal and ethical standards. This thesis describes the development of a prototype solution for the automation of filling suspicious transaction reports (STRs) to Singapore's Financial Intelligence Unit (FIU). The study examines the current process and time costs of filling STRs, and describes a solution, which gathers data from the company's internal microservices and transforms them appropriately to automate filling of the most repetitive parts in the STR. The developed prototype is tested with multiple reportable users, and the results show that when deployed it will reduce the time spent on the STR by half, whereas by improving the transaction data collection the time spent can be further reduced up to 75%. Apart of solution developed and expected to be deployed in May 2023, this thesis contributes to the field of FinTech by providing a practical solution for automating the filling of STRs to the FIU, reducing the burden on compliance officers, reducing human-errors, and improving the effectiveness of anti-money laundering efforts.
Graduation Thesis language
English
Graduation Thesis type
Bachelor - Computer Science
Supervisor(s)
Anastasija Nikiforova, Nikita Kuznietsov
Defence year
2023