Over the last ten years, complying with various regulations has become the front and centre mission for most financial institutions. Trade surveillance remains one such area depicting the growth and establishment of regulatory requirements. One of the primary features of this recent development is forcing firms to monitor and report market abuse almost immediately.
Regulators understand that certain market participants are essential for their smooth functioning. However, they also understand the need for surveillance and control over the market by sanctioning any non-compliers. Therefore, monitoring trade is no longer optional for financial institutions. Most major players today harness the power of AI and ML technology in their vigilant approach to market abuse.
Technology is among many staples in the future of trade monitoring and surveillance. Here is everything we think the future of trade monitoring holds.
An Era of Responsibility
New regulations require firms of all sizes to report potentially abusive behaviour. Europe’s Market Abuse Regulation (MAR) in 2017 marked the changing winds of trade surveillance, requiring financial institutions to showcase an effort towards making change happen. The goal is to eventually reach a state of real-time monitoring, with firms demonstrating the importance of understanding and risk assessment to avoid oversight.
The Dawn of AI and ML
Artificial intelligence and machine learning are indispensable tools for surveillance. Considering the ever-expanding and evolving trade practices and surveillance requirements, firms must automate processes to catch market abuse. Machines are also less prone to error than humans, making them ideal deployments for logical decision-making and understanding and adapting to various scenarios. Once programmed, they can recognise patterns and catch potentially abusive behaviour, flagging it for investigation.
Creating models that promote behaviours for efficiency in various roles can be more effective than trying to meet individual regulatory requirements. Automated systems can then be used across companies for effective surveillance based on inherent knowledge about how an individual should perform in a role. Using a model can also help with risk mitigation and catching possible abuse before it happens.
Promoting Accuracy and Relevance
ML can both identify potential abuse and aid in trade reconstruction. Therefore, it improves efficiency and accuracy while enabling new approaches. ML can also be better customised to suit the client’s needs and flag possible abuse in areas the client wants to focus on. Thus, it becomes more relevant to the company and the client while complying with the authorities.
Combining clients based on common characteristics into categories can further help the AI or ML system flag outlier behaviour. Especially when calibrated to different markets and peer groups based on models, the system understands what to look for and knows when someone is not complying.
Therefore, AI/ML join the charge alongside humans in identifying potentially abusive behaviour. Their accuracy and reliability also ease regulatory bodies’ tension while avoiding over-reliance since a machine cannot understand the nuances of a situation the way a human can.
It can, however, take care of some “grunt work”, allowing humans to perform an in-depth analysis of potential market abuse cases. Therefore, the future involves firms using technology and human resources to their advantage for better decision-making and regulatory compliance.
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