E-Comms Surveillance Deep Dive
Financial firms disagree on the definition of “realtime” and on the topic of who to monitor. They also highlighted enormous variability regarding their success with the application of machine learning.
Integration is Very Much a My WayYour Way Thing Company size, budget, and level of sophistication all influence integration. Some compliance teams are focused on improving their analytics so that they get fewer but higher quality alerts. In contrast, other teams are integrating financial crime, HR, social media feeds, and all sorts of other data types to create richer contextual maps that better inform alerts.
Real-time Doesn’t Always Mean Right Now One of the great needs around real-time surveillance is the ability to cross-monitor multiple e-Comms platforms in parallel. Doing so, and then using ML to identify any patterns in the activity, is one way to keep up with the bad actors who employ a multi-channel strategy as a means of thwarting detection of their market abuse. The technology here hasn’t quite caught up to this strategy yet,.
More Invitations to the Surveillance Party A natural progression on that path is to rank job roles or individual employees based on their respective risk profiles. From there, focusing surveillance activity and/or limiting it to those deemed highest risk creates numerous issues. Employment law, privacy issues, and heightened inquiries with the potential for additional audits by regulatory authorities all put the firm at greater risk – essentially the opposite of what was originally intended.
Machine Learning is Not a Silver Bullet It’s certainly not a cheap proposition, either. Companies are investing extraordinary sums and large numbers of personnel into machine learning. Doing so is required given the enormous data volumes, time, and effort required to properly train the search algorithms. This is no easy task and expertise is in short supply.
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