Written by Mary Tian on 26th of January
At an event on January 26th, Rav Kohli was interviewed by our President Jean Lacroix and offered an insider’s perspective on the ever-changing world of market surveillance, illuminating how technology, regulation, and data science intersect to protect market integrity.
Speaker Background
Mr. Rav Kohli’s experience spans decades. Previously, he sat as the Global Head of Surveillance Technology at TP ICAP, a prominent world leader in providing financial markets infrastructure and data. Additionally, Kohli has worked for 10+ years across Deutsche Bank, Citi, Barclays, and HSBC in surveillance, sanctions and regulatory analytics. Kohli holds a Bachelor of Science in business/managerial economics, information and technology at QMUL, a master’s in economics from UCL, and has completed a business programme at Oxford University.
His unconventional career path was shaped by graduating after the 2008 financial crisis. Though the situation then was a storm of financial chaos, many companies took this event as an opportunity to pre-emptively arm themselves for future risks, overall becoming more resilient.
After graduation, Kohli was unsure of what career to pursue, and initially “fell into banking” before proceeding in compliance, surveillance and regulation. His motivation, he explained, lies in ensuring markets function with integrity, particularly during moments of stress such as Brexit in 2016. Despite his journey being unorganic, he was able to find a novel career path for himself, demonstrating how unorthodox routes can often bring one to exciting destinations that would otherwise have been left undiscovered. Today, his focus extends to emerging risks in crypto markets.
Market Surveillance
Market surveillance is the process of automated monitoring and analysis of trading data to identify market abuse, such as insider trading, spoofing, and manipulation. This is often carried out by financial institutions, regulatory bodies, and exchanges. In the United Kingdom, the Financial Conduct Authority (FCA) enforces the Market Abuse Regulation (UK MAR).
Market misconduct comprises of unethical or illegal behaviour, such as manipulating the market, insider trading, and unlawful disclosure of inside information. Market manipulation distorts financial markets and are calculated to undermine other investors in a regulated market to give perpetrators an unfair advantage.
Kohli explained that market abuse, formerly confined to physical trading floors, has become increasingly fragmented with multiple data pipelines. With traders now operating across miscellaneous platforms and devices, surveillance teams face the challenge of reconstructing data behaviour across various entities. Now, the difficulty lies in gathering this data from numerous pipelines and standardising it for analysis, making the problem a “system-wide and industry-wide issue”. Thus, the task no longer involves spotting isolated trades but rather identifying system-wide patterns in highly liquid markets, often within seconds or microseconds, due to the growth of contemporary market surveillance.
To meet this challenge, modern surveillance relies on vast data pipelines that prioritise latency, quality, and standardisation. Since pattern detection is required at a speed faster than human comprehension is capable of, artificial intelligence is layered on top to provide context, detect anomalies, and reduce cognitive load. Kohli described a combination of techniques, including symbolic AI grounded in statistical probability and unsupervised learning models that help prioritise the most serious alerts. While AI has transformed present-day surveillance, he emphasised its limits, noting that it remains an early-stage tool. For example, Kohli stated that inspecting CCTV footage is a task that AI is yet unable to conduct, reinforcing that AI cannot replace all forms of human judgment.
Division of Roles in Market Surveillance
Rav elaborated on the role of juniors in market surveillance. He explained that juniors typically work on the collection of data within data pipelines, while the compliance team analyses the obtained information. Additionally, models (algos) are used as detection mechanisms to facilitate the detection of anomalies via alerts.
RegTech (Regulatory Technology)
Kohli introduced the concept of regtech, a subcategory of fintech. Regtech, or regulatory technology, involves market risk and preventing crimes such as money-laundering. The technology utilises AI, big data and machine learning to assist businesses in reaching regulatory requirements in an efficient manner by automating compliance, risk management and reporting. Regtech, when used properly can help companies reduce costs, increase accuracy and improve transparency. Additionally, Rav explained that regtech is worth around 15 billion, which composes part of the motivation for investor interest in fintech start-ups, as regtech is a subset of fintech.
Advice to Students
The session concluded with practical advice for students and junior professionals. Kohli stressed the importance of understanding real cases of market abuse, demonstrating judgment, and applying theory to practice through research, simulations, or technical projects. He suggested that students could manifest or construct synthetic data in simulated environments such as coding or GitHub to exhibit their understanding of risks to trading companies.
Rav underscored that, to stand out to recruiters, students must show more than high grades and skills. Therefore, the ability to apply theory to practice and evaluate market trade-offs is essential. He believes this expertise can be transferable to nearly any industry, opening doors to several prospective careers.
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