RegTech is not new, but the cost of complying with myriad regulations has pushed it into the limelight recently, write Frank Hatheway and Tony Sio from Nasdaq.
Existing vendors and startups alike offer solutions, and the technology has improved significantly over the last several years.
Like banks, many exchanges operate on decades-old legacy systems – some of which were inherited through mergers and acquisitions. Data is often stored in different formats and in disparate systems across the enterprise, and those systems do not necessarily talk to one another. That makes consolidating data for regulatory reporting purposes problematic.
This is not just an internal issue because it is also necessary to consolidate data from multiple exchanges for reporting purposes. To illustrate, NYSE, Nasdaq and BATS each are responsible for a significant portion of the on-market equity trading activity in the US. One can imagine the challenge of combining market data into a single view.
One way RegTech can add significant value is by extracting data out of technology silos, consolidating and normalising it, tracing the data lineage, and meeting regulatory requirements more efficiently. The applications should not only be considered within an organisation but also across a larger framework between regulators or self-regulated organisations.
Doing so enables exchanges and their customers to automate and streamline reporting processes – possibly to the extent of encapsulating regulatory reporting into the transaction process itself. Automation saves time, reduces errors and allows organisations to allocate human resources to higher value tasks. Moreover, it accelerates the time to market for introducing new products.
A positive trend is that the proliferation of RegTech is supporting cooperation between regulators and industry participants. Firms are educating regulators on new approaches to meeting their regulatory requirements, and they are demonstrating that better use of the technology provides greater transparency and faster turnaround on regulatory queries. The regulators are reciprocating as well. The UK’s Financial Conduct Authority’s regulatory sandbox, for example, allows exchanges and firms to test new products within a transparent and safe environment, and this is fostering greater understanding between regulators and exchanges. The sandbox is a recognition that regulators can use RegTech to do more with the wealth of data at their fingertips.
Importantly, there is an opportunity for RegTech to leverage machine learning and artificial intelligence. For instance, Nasdaq has several initiatives underway to incorporate it into the SMARTS surveillance platform. New technology in the upcoming version aims to predict regulatory analysts’ actions and evaluate the likelihood of potential abuse events. This will help reduce false positives and focus analysts’ activity on the most useful tasks. Through Nasdaq’s partnership with Digital Reasoning, cognitive computing is being integrated into SMARTS to create a holistic approach to surveillance with the initial focus on electronic communications and other unstructured data. Further, Nasdaq recently acquired Sybenetix, which takes a behavioural science approach to analysing conduct risk, with a view to incorporate these techniques into the surveillance suite.
The regulatory process is still very reliant on humans, but artificial intelligence can supplement human capabilities. In the short term, machines are unlikely to be able to ban someone from the market on the basis of their conduct or levy a fine. However, a surveillance system such as SMARTS can leverage artificial intelligence to generate better actionable information for human analysts.
Exchanges can also use artificial intelligence to improve their operating performance. Market operators’ systems are complex and distributed. The failure of a system component is not always a yes/no event: sometimes the performance of a system component is degraded. A well-qualified human technologist knows how to identify performance degradation before it becomes a crisis. Yet, machines can be trained to recognise a switch or a data port running too slow or packets being dropped at an abnormal pace, for example. Intelligent machines can suggest that a technologist go through certain steps to diagnose the problem, therefore enhancing the capabilities of less experienced technologists on the team.
Outside of RegTech, exchanges can leverage artificial intelligence to add value to their data. Nasdaq’s Trading Insights, for example, enables brokers in the US to compare their performance with their peers, and its Analytics Hub provides fund managers and traders with alpha signals to help augment trading strategies.
RegTech and artificial intelligence empower exchanges to address pain points in regulatory reporting, improve market surveillance, ensure reliability, reduce costs and introduce valuable products and services. Given these benefits, it behooves exchanges to investigate how they can make the best use of these technologies in their organisation.
Frank Hatheway is Senior Vice President and Chief Economist at Nasdaq.
Tony Sio is Head of Exchange & Regulatory Surveillance, Market Technology, Nasdaq.
They both took part in the recent WFE & Imperial College Technology Conference.