Jason Shiu is a Vanguard Asset Management Compliance Manager (Market Surveillance & Monitoring). He has multiple years of experience working in various aspects of surveillance, using different platforms and monitoring tools within the trade surveillance and e-communications space. Before Vanguard, Jason worked as a Senior Compliance Officer at GAM Investments and Eaton Vance Investment Management. He has completed a Legal Practice Course with an MSc in Law, Business and Management from the University of Law and graduated with a LLB Law degree from the University of Birmingham.
Through this article, Jason Shiu, Compliance Manager at Vanguard, explores the intersection of artificial intelligence (AI) and surveillance in financial services. He examines AI’s potential to automate and optimise various surveillance workstreams, including trade surveillance, e-communications monitoring, voice communications analysis, and personal dealings oversight while emphasising the continued importance of human input in the surveillance process.
As we hit our stride into the year ahead, one theme seemingly appears on every hot topic agenda item—Artificial Intelligence (AI)—in the case of financial services; the general presupposition is that current processes can be automated and/or optimised. However, to the extent that these processes are automated and/or optimised—will we get to a future state of optimisation where human input is not required? This will not be the case for the future state and will require a blend of AI integration and surveillance personnel input.
Use Cases Of AI In Surveillance
Within financial services, surveillance can encompass various workstreams involving trade data, written e-communications, voice communications, and personal dealings (conflict of interests)—and in the context of this article, the use case can be assumed to be based upon best execution market abuse and conduct-related matters. We will touch on each of the workstreams in turn:
Trade Surveillance—fundamentally, this type of surveillance generates alerts based on a parameter or threshold for a particular test. Where these alerts are generated, the individual evaluating would typically close the majority of alerts based on a false positive classification for various reasons. Now imagine where AI can be integrated; trade alerts being generated that were historically considered false positive would not even appear, and the individual would solely focus on the actual positive alerts – this may be due to calibrated AI factors such as market sentiment, trade history of the dealing desk, and how surveillance personnel have historically closed alerts (to name a few).
“AI Will Play A Crucial Role In Surveillance, But The Future State Of Optimisation Requires The Human Element To Refine Secondary Review Processes And Investigations”
Written E-Communications—with the turn of the century, particularly from 2010 onwards, e-communications have become commonplace in everyday life. With hundreds of billions of messages being sent and received each day (estimate just for e-mail alone), it is easy to imagine the oversight difficulty in financial services even with a task force of surveillance reviewers. Although lexicon-based monitoring has been a valuable tool in filtering out the noise of e-communications, there have been deficiencies in how it generates alerts for review. This is where AI gains its main stamp of approval – the ability to contextualise messages and compare with lexicon-based triggers has meant firms that integrate AI with e-communications monitoring can review better alerts compared to firms purely using lexicon-based tests.
Voice Communications—Similar to Written E-Communications, the use of AI in this monitoring space has gained dramatic momentum since 2024. Like written e-communications, there is an incomprehensible number of hours of voice recordings within financial services based on regulatory obligations. The oversight surrounding this type of monitoring typically revolves around sample monitoring, targeted monitoring, or an automated system to oversee relevant persons. Historically, the theory of transcribing a call using software and being able to review a transcript was seen as a potential solution to voice communications monitoring. This then moved to transcribed recordings being integrated into existing written e-communications systems. However, the main limitation was always the accuracy of the transcriptions in voiceto-text. When dealing desks used short-abbreviated financial terms (standard practice) and/or spoke in different languages or accents, transcription capabilities would often fall short regarding accuracy. With the exponential improvement of AI in the last 18 months and multiple other large language models (LLMs) being utilised, voice transcription services with AI integration are beginning to be able to rapidly learn and contextualise relevant conversations in a repeatable manner accurately.
Personal Dealings (Conflicts of Interests)—the challenge with this workstream in the context of personal account dealings, gifts and entertainment, and outside business activities, from my own experience, has always been the accuracy of information being inputted by the employee; unfortunately AI in its current form is unlikely to assist this aspect in the near term. However, assuming this input is accurate, to begin with, the second issue that surveillance functions face is typically the identification of alerts alongside other recorded activities. Historically, where there have been control room-related incidents, the difficulty has been the timely identification of other related workstreams, i.e. trading and/or communication events. With AI integration into these functions, the historical challenge for surveillance personnel to cross-identify other related incidents becomes less problematic and more systematic. The caveat is that the AI integration is on a viable platform that ingests more than one workstream of data.
Conclusion
In summary, AI will play a crucial role in surveillance, but the future state of optimisation requires the human element to refine secondary review processes and investigations. While AI continues to develop and evolve, the core principles of surveillance governance and framework will remain rooted in traditional practices.
