Compliance Playing Catch-Up with Regulators in Data Analytics Race

April 19th, 2017

An article by Charles Hastie, Regulatory Head, Clutch Group, entitled “ Compliance Playing Catch-Up with Regulators in Data Analytics Race” has been published by Legal Tech News. An excerpt of the article has been pasted below.  

Compliance Playing Catch-Up with Regulators in Data Analytics Race

As the regulators’ own capabilities of examining big data grows, it becomes less likely that they will discuss search terms with you to narrow down a data request.

Charles Hastie, Clutch Group, Legaltech News April 19, 2017

Would you like to hand over an enormous dossier of your own data to a regulator before knowing what it contained? And what if you did not know the lines of enquiry they planned to pursue? To board members and senior legal and compliance executives, that would constitute a nightmare scenario where the risks are multiplied by “unknown unknowns.” And yet, this is becoming increasingly likely as certain regulators enhance their data analytic capabilities.

What’s changed?

In recent times, data analytics has found broad use by both firms and regulators, primarily in the context of enforcement and litigation. In this area, firms have typically been ahead of the regulators. This is because of the general dynamic of reactive regulatory investigations—the regulator effectively instructs firms to conduct all pertinent discovery, and it is therefore incumbent on the firms to interrogate the data and arrive at the necessary facts. As a result, the firms surged ahead in terms of technology adoption, diving into the markets to partner with the best available analytics partners.

The use of data analytics has been less evident in a supervisory context, where the objective of review is to identify misconduct on a proactive basis. But, as a close observer of the industry, I was confident that the firms were likely to be ahead of regulators in this area as well. However, at the Annual Seminar for Legal & Compliance professionals in the Securities and Financial Markets industry (SIFMA) in March, it became clear that some regulators are making data analytics a big priority. In contrast to many firms, these regulators have dedicated vast resources to leveraging analytics for supervision and examination, and that commitment is already delivering results.

This sea-change has meant that in the field of proactive analytics it is the firms that risk being left behind by the regulators.

What are the regulators up to?

Big-name regulators such as the SEC and FINRA are throwing their weight behind data analytics. The SEC’s Division of Economic and Risk Analysis (DERA), created in 2009, is tasked with integrating financial economics and rigorous data analytics across the entire range of SEC activities, including policy-making, rule-making, enforcement, and examination. This mandate has been backed with resources; DERA has 173 staff, 80 of them with PHDs, and 20 of whom are “quants”. $67 million was spent on DERA in fiscal year 2016 alone.

FINRA is not far behind in its commitment to prioritizing data analytics. The most recent annual report talks about the huge growth in data processed and monitored (75 billion pieces of market lines of data a day) by the organization. Processing and monitoring this volume of information has been made possible by the agency’s use of cutting-edge technologies.

FINRA’s use of data analytics has enabled them to pinpoint high-risk brokers for heightened surveillance. In 2015, this led to targeted registered representative exams, with 20 referred for formal disciplinary action. The agency is also using advanced technology to collect and integrate trading data across exchanges and alternative trading systems, then running surveillance patterns and threat scenarios to look for misconduct such as layering, spoofing, algorithmic gaming and wash sales. Consequently, FINRA’s Market Regulation department made 142 referrals to the SEC in 2015.

These tools and technologies employ machine learning to analyze data associated with misconduct, learn the related patterns, and then review new sets of data to see if the misconduct footprint is repeated. Data sources include trade data and Blue Sheet data gathered through Enforcement and Examination activities. The advanced analytic tools in the back pocket of the regulators include Support Vector Machines, Clustering, and Random Forests.

Th full version of the article is available on LegalTech News.