Beginning in May 2024, the FCA ran a 3-month Market Abuse Surveillance TechSprint to explore how advanced solutions leveraging artificial intelligence (AI) and machine learning (ML) could help detect evolving forms of market abuse.
The TechSprint aimed to improve market surveillance in firms and make processes more resilient.
We were particularly interested in seeing how new technologies, such as AI can help accurately identify more complex types of market abuse that are currently difficult to detect, such as cross-market manipulation.
Nine teams from across the globe including Belgium, Australia, Ukraine, USA and the UK worked together over the 3-month period. At a Showcase Demo Day on 20 July 2024, teams presented solutions focused on eliminating harm and ensuring a fair and competitive market.
Over 200 representatives from RegTech and AI vendors, public sector agencies, academia, major banks and senior representatives from the FCA attended on the day.
You can see video recordings of each team’s presentation below.
Problem statements
The TechSprint aimed to tackle 3 set problem statements:
- How can AI and advanced analytics improve accuracy and frequency of post-trade market surveillance alerts and signals.
- How can AI and advanced analytics help identify instances of complex types of market abuse that traditional rules-based surveillance tools struggle to identify.
- How can AI and advanced analytics help identify market and trading anomalies indicative of market abuse, manipulative strategies, and disruptive trading practices.
Participants were provided with access to 1TB of pseudonymised markets data, including transaction reports, order books, news feeds, and price feeds, facilitating the development and testing of advanced AI/ML models in the data-rich environment of the Digital Sandbox.
Showcase Demo Day
The solutions developed over the 3 months and presented during the Showcase Demo Day included:
- AI/ML models such as isolation forests to reduce false positives and improve the accuracy of alerts, ensuring genuine cases of market abuse are identified more reliably.
- Anomaly detection techniques such as Bayesian Network Analysis were employed to identify unusual trading patterns indicative of manipulative strategies and disruptive trading practices based on historical data.
- The application of econophysics theory and kinetic energy models to identify subtle price changes in orderbook data and large language models to contextualise outliers and filter out false positives.
