Helen Bevis, SteelEye


There has been many regulation changes to hit financial institutions since 2008, so we poised the question to Helen Bevis, Business Relationship Manager at award winning compliance solution provider, SteelEye. What does the evolution of trade surveillance look like today?


What are the key drivers behind the adoption of a trade surveillance system?

There is a clear legal and regulatory drive for financial firms to implement trade surveillance systems to ensure they comply with regulations, but also to reduce the risk of fraudulent malpractice & protect their reputation. The introduction of The Markets Abuse Regulation (MAR) has significantly raised the bar for compliance and surveillance systems to not only deliver rule based detection, but ensure compliance officers have a personal liability to continually evaluate their company’s trading activity.


by Helen Bevis, SteelEye

To achieve comprehensive surveillance coverage, firms now need a solution that can bring together disparate systems, so that all data can be evaluated in a centralised repository, allowing  analysis  to be run across multiple asset classes and products, going above and beyond the “tick in the box” solution. MAR came into force in July 2016, and we are still seeing firms install their first deployments of an automated system. This could be due to the increase in physical fines being handed out, or that technology advancements have made compliance systems more attainable and more practical.


How do you manage the challenges of tracking and storing multi-channel communications (Chat, text, voice, Email)?

Correlating trade alerts with communications (voice & e-comms) prevents analysts from chasing down false positives and enables them to detect the underlying intent for malpractice. It’s better to start with multiple pieces of evidence which can be stitched together than just one rule based alert. Extending your analysis to include trade reconstruction produces an entire auditable trail of evidence which can be reviewed together. Having the ability to construct these types of multiple events in a matter of minutes, firms can utilise it every day giving them the ability to generate useful insights and performance guidance.


For any of this to be possible you require a platform that can easily ingest, index and make searchable all communications data across all devices using a tried and tested data on boarding method. Additionally utilising a level of machine learning helps to accelerate the results and fine tune to your business needs.  SteelEye is able to reduce all these frequent challenges using a common framework across its analytics, database and case manager, reducing the physical work flow, resources and cost.  


What is an effective consolidation of data and how smart can you be with the data?

All compliance officers are looking for a magic spotlight to highlight where the suspicious activity is in their business. Having the data all in one place is the first step, but having the right analytics to make use of this data repository is essential. Every business is different and therefore no “one size fits all” out of the box rule, which would adequately cover what the regulations demand. Each installation requires an easy to use, but flexible tuning mechanism, so users are able to customise rules and thresholds to suit the firms trading activity. SteelEye also provides its users the “Hindsight” ability to back test scenarios and review the results before being processed into production. Giving compliance officers the powerful insight to know what their results would have looked like if the conditions were different, allowing users to understand change, and how to forecast results.


What is the role of Artificial Intelligence (AI) & Machine Learning (ML) in helping deliver better solutions?

AI and ML are common practice in financial technology these days… but it’s how they are being used that people are confused about. In trade surveillance the technology has come to the forefront showing us how we can learn from the patterns in our data. If we can establish what is normal then we can focus and learn from where there are deviations. Understanding the behaviour of individuals can reveal not only conduct risk and exposure to dependencies, but also forecast areas of concern, which can then be fed back into the analytics. Overlaying multiple different sources of data and bringing them together will ultimately build a solid foundation of investigation.


There is still much evaluation to be derived from the evolution of technology, but having a state of the art core platform which can be built and adapted to these new trends, is the key to unlocking its potential and benefiting from it as it starts to grow.




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