Trade surveillance is undergoing a seismic shift. Once viewed as a regulatory formality, it has become a cornerstone of strategic risk management in global finance. As markets grow more complex and interconnected, the ability to detect, interpret, and act on suspicious trading behavior in real time is no longer optional; it’s essential. The convergence of AI, automation, and advanced analytics is transforming how firms approach surveillance, enabling them to move beyond compliance and toward proactive risk mitigation.
This blog explores key insights from the Trade Surveillance Revolution report by Datos Insights, commissioned by Eventus, highlighting how leading Tier 1 banks and broker-dealers are adapting to this new reality.
The new era of trade surveillance: from compliance obligation to strategic priority
In today’s hyper-connected financial markets, trade surveillance has evolved from a regulatory checkbox to a strategic necessity. As firms face increasing data complexity, regulatory scrutiny, and market volatility, the need for smarter, faster, and more adaptive surveillance systems has never been greater. The Trade Surveillance Revolution report by Datos Insights, commissioned by Eventus, offers a deep dive into how global financial institutions are navigating this transformation.
Firms are contending with a range of persistent issues, including data fragmentation, with many large global banks reportedly using over 10 different systems to monitor trades across asset classes, leading to inconsistent alerting and compliance gaps. False positives are another major concern, with most firms experiencing rates above 25%. Many firms still rely on outdated platforms that lack API integration, making it difficult to automate workflows or scale surveillance across regions.
AI and automation in action: real-world use cases transforming surveillance
Artificial intelligence (AI) is proving to be a powerful ally in reducing noise and enhancing detection. For example, a large investment bank implemented machine learning models to identify rogue trading behavior, resulting in an 80% reduction in false positives and faster escalation of high-risk cases. Surveillance teams are also combining trade alerts with email and chat data. One firm flagged a suspicious trade after correlating a trader’s messages with unusual order patterns, something traditional systems would have missed.
Surveillance maturity varies widely across asset classes. Equities show the highest satisfaction levels due to longstanding systems, though one firm noted that replacing its equity surveillance platform would require over six months of regression testing due to the volume of trade.
In contrast, fixed income surveillance in the EMEA and APAC regions is hampered by fragmented data and a lack of centralised reporting. Another firm cited difficulties in monitoring bond trades due to inconsistent timestamping and missing reference data. Swaps and alternatives pose unique challenges, with one US firm reporting that pricing data for FX swaps was often unavailable after internal cutoff times, leading to gaps in surveillance.
Strategic shifts and workforce evolution: building smarter systems and smarter teams
The report outlines several forward-looking strategies for modernising trade surveillance. One top-tier bank is developing a unified model to correlate alerts across equities, derivatives, and crypto, helping detect complex manipulations like wash trading and spoofing across markets. Another firm migrated its surveillance infrastructure to the cloud, enabling real-time processing of over 10 million trades per day while reducing infrastructure costs by 30%. A mid-sized broker-dealer opted for a hybrid approach — building core capabilities in-house while leveraging vendor platforms for niche asset classes like digital assets.
Manual workflows are a major bottleneck in case management, but change is underway. A firm piloting large language models (LLMs) now auto-generates case summaries, reducing the analyst workload by 50%. Another institution replaced its legacy case management system with an API-enabled solution, cutting alert triage time from hours to minutes. Firms are also implementing independent QA sampling, with one US bank now reviewing 10% of closed cases weekly to ensure consistency and accuracy.
On the talent front, surveillance is no longer just for compliance officers. One firm’s surveillance team includes ex-traders, data scientists, and behavioral analysts, helping uncover nuanced trading behaviours. Compliance professionals are learning Python and advanced Excel, and one firm reported that its analysts now build custom dashboards to track alert trends and KPIs. Vendors are offering certification programs, and one firm mandates quarterly training on new surveillance features to keep its team up to date.
Future-proofing surveillance: investing in innovation for long-term resilience
To future-proof their systems, firms are investing in capabilities that will define the next decade. A leading firm is developing models to link equity and derivative trades, helping detect front-running and insider trading. Some institutions are automating market replay for FX and swaps to reconstruct trading scenarios and validate alerts. One firm uses AI to tag relevant news articles to trades, helping analysts understand the context behind unusual activity.
The adoption of APIs is becoming a cornerstone in the modernisation of trade surveillance systems, particularly as firms seek to overcome the limitations of fragmented legacy infrastructure. According to the report, one of the key challenges in case management is the “lack of API integration in legacy systems,” which restricts automation and seamless data exchange between surveillance tools and case management platforms. This limitation contributes to inefficiencies and operational risks, especially as documentation and workflow processes remain largely manual.
The report also identifies the implementation of “API-based case management systems” as a critical solution, enabling integration with external compliance tools and supporting scalable, AI-enhanced workflows. Furthermore, the broader challenge of integrating data across disparate systems is compounded by the “lack of API connectivity”, underscoring the need for standardised, interoperable architectures. As firms transition toward more agile surveillance ecosystems, API adoption is not only enhancing operational efficiency but also enabling real-time data ingestion, improved alert analysis, and more responsive compliance capabilities in an increasingly complex regulatory environment.
The trade surveillance revolution is here. By embracing AI, improving data governance, and investing in talent and technology, financial institutions are transforming compliance into a strategic advantage. The future will belong to firms that can detect risk in real time, adapt to change, and turn surveillance into insight.