By Simon Dix, CEO and founder at DX Compliance
The UK’s Market Abuse Regulations came into force in on 31 December 2020 and, while these are similar to regulations in other jurisdictions, both the number and value of fines for failing to meet requirements have risen significantly over the last two years.
This poses a real commercial risk for financial services businesses, providing strong incentives foo review compliance processes.
However, this is easier said than done for buy-side companies and asset managers, many of which rely on software developed for stock markets and sell-side professionals. This brings problems for buy-side firms more interested in orders than trades: Workarounds may result in alerts being turned off when they generate too many irrelevant notifications; while existing software fails to draw the ideally-required data across traditional departmental silos, limiting the quality of analysis and risk identification.
So, how can buy-side firms make sure they have the systems and controls in place to protect their business?
First we need to understand what market abuse might look like in this setting: Insider trading, market manipulation, and front running are all activities that buy-side companies should proactively look for.
Next we need to acknowledge why buy-side firms can struggle to identify market abuse. We have identified several consistent issues including problems monitoring every order correctly, false positives feeding inefficiencies, quality of data governance, issues navigating data ownership and completeness, and challenges around effective model-testing systems and processes.
So what are the solutions?
In our view, AI and machine learning hold the key to changing the compliance landscape for buy-side professionals. The FCA has already integrated AI into its surveillance approach and its ability to process huge amounts of data in almost real time could help organisations spot suspicious activity more quickly, building compliance trails more easily than before.
Pattern recognition can help asset managers identify useful trends and anomalies in trading data that may give cause for concern. Quicker, more in-depth behavioural analysis also allows companies to monitor trader behaviour across a wider variety of channels, increasing their ability to detect deviations from the norm.
Support fro cross product surveillance also makes it easier for AML professionals to detect specific market abuse typologies, such as those involving bonds and equity, spoofing patterns, pump and dump scenarios, and manipulation of stock price to profit form options.
The increased speed at which AI and ML-based surveillance systems can be set up also facilitates enhanced testing and configuration prior to system go-live while, by monitoring all major asset classes at once, AML professionals can review a single source of information – reducing unnecessary noise and allowing a high-degree of parameter tailoring along different asset classes.
Crucially, AI’s ability to process huge quantities of data in almost-real time means ‘red flags’, such as suspiciously large order volumes or unusual order frequencies, will be picked up more quickly, alongside more-complex activities such as cross-product or cross-market manipulation.
Investing in AI or machine-learning led software offers a reliable, cost-effective route to ‘getting it right’ that can evolve with the needs of any financial business, large or small. This software can ingest different data points; combine it; run it through proprietary risk engines to identify higher-risk behaviours; auto-generate workflows without breaking audit trails; and guide professionals through the decision-making process, finally empowering buy-side asset managers to navigate complex compliance challenges with greater confidence and precision.
