Modern regulatory compliance has led to a convergence of regulatory standards on a global basis, reflected in similar requirements being implemented across multiple different regions. Regulators are expecting a high degree of self-regulation, which requires a holistic approach to surveillance and risk controls. There is a clear need to analyze a substantial amount of structured and unstructured data, and the ability to filter this is beyond the capacity of compliance teams alone.
How can compliance teams navigate through this massive amount of data to better identify the intent, context and behaviors behind trading activities? Nasdaq, Digital Reasoning and TABB Group tackled this topic in a recent webcast. Terry Roche, Head of FinTech Research at TABB Group, moderated the discussion, along with Michael O’Brien, Head of Product Management for Risk and Surveillance Solutions at Nasdaq, and Bill DiPietro, VP of Product Management at Digital Reasoning.
Key takeaways from the webcast include:
1. Regulatory Convergence Presents a Need to Monitor Multiple Different Data Sources
Today’s regulatory environment has led to a higher prioritization of compliance. With a zero-tolerance policy, the industry has experienced record-breaking fines, which has led to increased spending on compliance. In order to comply with these new standards, there is a greater need to understand various forms of data, bringing new players into the picture, such as Chief Data Officers.
Michael O’Brien discussed how the notion of intent is critical to establishing the motives behind a specific trader’s actions. Participants of the Webcast indicated that their top priority in monitoring risk is identifying possible collusive conduct indicating intent to engage in market abuse.
2. Increasing Data Quantities Lead to New Challenges
The massive amounts of data created on a daily basis are impossible to filter in an efficient manner by humans alone. The prominence of false positives and irrelevant signals suggests that a more efficient solution must be implemented. As Bill DiPietro explained, data is an asset that can be leveraged to create strategic advantage through the use of Machine Intelligence.
However, the FinTech industry has a first-mover disadvantage. Many firms are still analyzing data through single channels, but these silos must be integrated and organized in order for compliance teams to get a truly comprehensive view of trading activities. Nasdaq and Digital Reasoning offer a holistic approach to integrating these sources of data in order to efficiently monitor and organize patterns within the data.
3. The Importance of Understanding the Context of Data
It is clear that trade data alone is no longer enough. Firms must analyze additional data sources, such as e-Comms and a-Comms to determine the context and intent behind specific behaviors and patterns. SMARTS Trade Surveillance and Digital Reasoning provide a contextualized and intelligent surveillance model to understand data in its various forms when integrated. This works across many asset classes in order to reveal potential illicit activities through pattern recognition. The system is constantly learning from inputs, seeking to improve its efficiency and accuracy.
Through Natural Language Processing (NLP), the content of electronic and audio communications is analyzed and the context is interpreted. When matched with trade data, suspicious activities can more rapidly identified and prioritized, allowing compliance teams to more efficiently manage alerts. The key is to separate “signal” from “noise” in order to reduce false positives.
Nasdaq SMARTS Trade Surveillance and Digital Reasoning have revolutionized surveillance capabilities by finding meaning in data patterns and relationships. This has enabled compliance teams to prioritize investigations and take preventative action.
The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.