A fresh approach to trading compliance
Harry Stahl | Director, Business and Solution Strategy, FIS
March 08, 2021
The challenges of compliance in the trading space are familiar – there’s both the operations and technology burdens and the massive potential costs of fines and sanctions for failures. The pressure will only increase if regulators increase pressure on current areas or expand scrutiny in areas such as eCommunications. But if you can take a step back and look at the bigger picture, you have the opportunity to take a fresh approach and make a bigger impact.
If you treat trading compliance as a RegTech ecosystem – in other words, a group of interrelated systems and teams – there are clear opportunities to quickly increase accuracy, streamline process and engineer for the constant change we face.
Look at the solutions at each stage of the transaction life cycle – from pre-trade compliance checking and decision-making, to trade surveillance, AML, and post-trade reporting, and finally eComms surveillance.
Then consider the most effective technology for each. For instance, a smart logic engine can transform complicated pre-trade decisioning into an automated streamlined flow with clear references to the underlying regulatory rules for each decision. Machine learning can find patterns in large volumes of data to dramatically increase the accuracy and reduce the noise in both AML and eComms surveillance.
Finding smart point solutions is a good first step, but then recognize what we all know – that problems show up in multiple ways in multiple stages of the trade life cycles. For example, if your AML system finds a shady actor behind a transaction, it’s a clue to look for patterns related to that trade, including what may be there to discover in your eComms surveillance data. Similarly, if you see a problem in eComms, you can go to AML and check transactions for suspicious activity, and so on. By looking across the ecosystem, you can dig deeper and get clearer results.
It’s a lot to solve, but a smart response doesn’t require a massive investment or a shared data lake. But at a minimum, systems need to interoperate to trigger alerts and create a virtual case management across the ecosystem.
In the pandemic and post-pandemic world, everyone is has learned to keep close track of the factors that affect operational resilience. In the trading RegTech ecosystem, the obvious ones are volume and market volatility, increased levels of cyber fraud and the risk of money-laundering and funding of terrorism, as well as potential for continuously evolving regulatory requirements.
But the RegTech ecosystem we’re looking at here isn’t just more accurate and cheaper to run. It’s also more resilient: loose enough to allow quick changes to individual solutions and connected enough to create coherent new solutions. On top of that, add regulatory and market monitoring to identify changes early.
Now, you can take out risks and cost while improving accuracy today – and be ready for regulatory change tomorrow.