Everyone’s heard of KYC (Know Your Customer), but what about Know Your Subscriber or Know Your Professional—the need for an institution to understand not just how many internal staff or external clients are consuming data that it licenses, but who those individuals are, and whether they should be paying full-price professional rates or lower, non-professional fees.
For any firm with a mix of institutional and retail client bases, the difference can have a significant impact on the firm’s overall data budget. And getting these designations wrong can incur costly penalties.
For example, CME Group charges $105 per month for professional access to each of its marketplaces—CME, CBOT, Nymex, and Comex—a total of $420 per month per professional access for all four markets. In comparison, a non-professional trader would pay only $1 per month for top-of-book data from each market (or $3 if they take all four in a bundle), and just $5 per exchange for market depth (or $15 as a bundle).
“We handle reporting for several firms that have institutional and wealth management and retail businesses, and want to understand their professional and non-professional usage,” says Barry Raskin, managing director at consultancy Jordan & Jordan. “Market data is usually reported centrally, and is often handled by central finance departments who are under all sorts of pressure to keep costs down. The whole of market data is usually under that function. So having a good handle on your users and whether they are pro or non-pro is critical to keeping costs down, because you don’t want to pay professional fees for non-professional users.”
However, determining whether an individual should be classified as a professional or non-professional user is easier said than done. Not only do definitions vary by exchange, they can also be counter-intuitive. Just because an individual is a retail investor trading their own money does not automatically mean they’ll be recognized as non-professional. If they hold a professional financial certification, or if they registered their account using a business address or email, they may be deemed a professional investor, and therefore be subject to higher fees.
At the definitions level, there is still dissatisfaction about some cases. For example, an executive assistant at a bank may be registered with regulators but does not trade as a professional—but if they answer an exchange’s questions correctly, they would probably be classified as a professional.Tom Davin, FISD
Last year, Desjardins Securities (Disnat), the discount brokerage business of Desjardins Group, one of Canada’s top 10 financial firms, encountered this challenge as part of a project to migrate away from legacy platforms run by Nexa Technologies. Disnat acquired the Nexa business from Penson in 2013 prior to the service provider’s collapse.
The objective of the acquisition was to protect the platforms it had been using, but ultimately to wind down and close the legacy Nexa tools, and bring all technology management in-house, and establish direct relationships with exchanges for market data certification, says Richard Tardif, expert advisor for markets technology at Desjardins. In addition, the firm planned to have Disnat take over functions such as market connectivity, front-end development, building an entitlements system, and performing monthly reporting.
As a smaller firm, rather than devote senior data execs or hire dedicated staff to perform repetitive number-crunching tasks, Disnat outsourced much of its market data management function to New York-based consultants and service provider MDMS.
Laying Down The Law
After about a year, as the firm started reporting, it realized the importance of reporting professional and non-professional usage, Tardif says—not only because of the cost differential, but also to avoid the need to dig up client records years down the line if the firm found itself subject to an exchange data audit. He warned management that exchanges will assume everyone is a professional unless the firm could prove otherwise, adding that in the event of a dispute, it might have to pay professional fees plus any retroactive fees.
Since the legacy Nexa toolkit—which has since been wound down—did not include any tool for performing in-depth analysis to differentiate professional and non-professional users, MDMS introduced Disnat to D8A Force (pronounced “DataForce”), a New York-based software vendor founded by Avinaash Bhuvaneshwar, a former market data executive at Nomura, Goldman Sachs, and Credit Suisse, who set up the company to address the pro-versus-non-pro issue.
“When I joined Nomura as exchange relations manager, I started attending FISD and SIFMA industry meetings, and everyone was talking about pro and non-pro. Nomura had no skin in that game, because it has no retail business, and I wanted that conversation to end so we could talk about more relevant things, so I started doodling ideas. When I bounced them off friends in the industry, I realized this was a big thing. So in December 2015, I left Nomura and founded D8A Force,” Bhuvaneshwar says.
The resulting KYP (Know Your Professional) platform uses artificial intelligence to determine whether a customer—for each exchange, recognizing the differences in exchange policies—should be reported as a professional or not. On day one of an implementation, KYP can process a firm’s entire existing subscriber base automatically in a fraction of the time it would take manually (estimated at up to 10 minutes per subscriber), and maintain that as new clients are onboarded.
At the start of 2018, D8A Force started working with MDMS. Bhuvaneshwar wanted to focus on the larger end of the market, while MDMS wanted to tackle more modest clients. Their agreement allows MDMS—with support from D8A Force—to use KYP to service retail brokerages with fewer than 100,000 accounts.
As a result, Tardif says Disnat is now in control of its data spend, which is not just good practice, but in practical terms gives the firm a clear picture of what’s in its entitlement system, and the ability to match exchange data invoices on a user-to-user basis, whereas Nexa billed in a blended rate for all services, so it was hard to understand how much data on its own was costing the firm
In addition, having greater control over data usage means the firm has been able to reduce real-time data usage and fees for clients who don’t need it. Now, on a monthly basis, Disnat re-challenges users who consume real-time data but don’t trade a lot, enabling the firm to slash the number of users of real-time data—either because they didn’t use it, or because using it would have designated them as a professional user.
While the firm hasn’t calculated the savings—or savings from audit-cost avoidance—he says that around 20,000 customers may consume real-time data. Of that number, the KYP tool verifies that Disnat correctly classified 90% of users, and it also helps with the remaining 10% grey area that need extra work to ensure they are properly designated.
D8A Force isn’t the only specialist player in this space. New York-based Prosparency is a service provider set up in 2016 specifically to address the pro-versus-non-pro classification issue. The company was founded by Weijian Zeng, who had previously worked as director of broker products and director of data management at Enso Financial Analytics, and as a senior developer at Interactive Brokers.
In those prior roles, he met Sara Banerjee—then director of data strategy execution at SIX Financial Information, who had worked at SIX and its predecessor, Telekurs, since 1989. With Banerjee’s experience of data issues, and Zeng’s technical expertise, the two joined forces and created the KYS (Know Your Subscriber) platform.
Like Bhuvaneshwar, Zeng became familiar with the pro-versus-non-pro issue—and Banerjee—as a result of her involvement in data industry association FISD’s working groups addressing the topic. FISD managing director Tom Davin notes that even with industry-wide participation, the issue’s complexity means it has remained unsolved since it first emerged along with the internet boom.
“Back in the 1980s, a non-professional getting access to real-time data would have been prohibitively expensive. But when the internet came into being, and online brokerages began offering trading over the web, there was essentially no connectivity cost for non-pros,” Davin says.
However, the dot-com crash and, later, the post-credit crunch financial crisis may have contributed to exchanges’ laser focus on non-professional usage. “It may have made exchanges more focused on compliance where professional users might be signed up as non-professional. If an online brokerage has millions of users and an exchange suspects that some of those are actually professional users, that’s a significant amount of money.”
Now managing director of Prosparency, Banerjee concurs that the issue has come to the fore in recent years as a result of the growth in online trading, and the need for brokers to verify thousands of clients to ensure compliance with exchange data policies, using still-largely manual processes based on honesty statements. Without an automated tool to perform this validation, firms simply can’t keep pace with the requirements of their data providers, she says.
KYS uses machine-learning algorithms to categorize subscribers into professional and non-professional groups using a rating scheme—or a “probably” category, where gray areas still exist—by comparing data on individuals captured from public sources and social media against the rules of information providers and exchanges.
Prosparency has seen up to 20% of some firms’ user bases unclassified or flagged as potentially misclassified.
“When you’re dealing with tens of thousands or hundreds of thousands of subscribers, having a conversation with each one isn’t possible,” says Joe McAlinden, director of operations at Prosparency. “We measure our success in terms of the time saved. It depends on the size of a client’s team, but if a firm has to go through 15% to 20% of its clients manually, it could take weeks or months. But 90% of that work can be alleviated by our product. Then the client has to go back and have a conversation with their customer, which can be difficult, especially if they’ve been untruthful—for example, the job they reported doesn’t match what they listed on social media.”
It may seem ludicrous, but multiple sources report instances of individuals who list their profession as a trader to impress others, and put themselves at risk of having to pay professional-level fees, while also increasing the compliance burden for their brokers.
Prosparency’s clients range from large to small brokerages, as well as well as information providers trying to identify professional users misclassified as non-professionals. The vendor counts ICE Data Services and Interactive Brokers among its clients.
ICE declined to comment for this article, though ICE Data Services president and COO Lynn Martin has said previously that “companies like Prosparency… help us gain better insight into the types of subscribers utilizing our market data and trading platforms.”
Not So Trivial Pursuit
This kind of insight is crucial for data providers and consumers alike. To a large firm, the cost differential can seem trivial until an exchange claims an error going back three years and someone tallies the cost multiplied by, say, 1,000 clients over 36 months, and then the firm has to go back and investigate customers claims and how they would qualify under the exchange rules, says Bill Lee, senior market data advisor at Interactive Brokers.
Users always say that if exchanges simplified their rules, they’d get paid on time. I suspect very few people are cheating. Most people want to be compliant and pay when they should pay, but aren’t in a position to hire people to make it work—there are a lot of resources involved.Bill Lee, Interactive Brokers
Suddenly, the costs aren’t so trivial anymore, and exchanges can be (justifiably) aggressive about enforcing their fees, but sometimes seemingly without an appreciation for the amount of work required by brokers.
“Users always say that if exchanges simplified their rules, they’d get paid on time,” Lee says. “I suspect very few people are cheating. Most people want to be compliant and pay when they should pay, but aren’t in a position to hire people to make it work—there are a lot of resources involved.”
Also, blame can be laid on both sides of the fence, he adds, citing cases where an exchange auditor will say a user should pay professional fees because their address is that of a brokerage firm, but who works in a non-trading role at a different, non-financial company that leases space in the same building. “I don’t think any of that is malicious, but much of it is contentious,” he says.
To prevent issues—which, if a large firm is found to have wrongly classified users over a period of time, could prove costly—from arising, Interactive Brokers has set up an 11-step process specifically to ensure it is classifying professional and non-professional users correctly. First, when a client opens an account, the firm runs the usual KYC checks required by regulators.
Then, it takes the information provided by the customer and runs it against its own client data to look for similarities, such as using the same email address, or the same family name as listed on other accounts. Then it provides the data to Prosparency, which checks it against 140 global data sources, and delivers a determination of whether a user should be reported as a professional or not.
“If everything else fails and we cannot determine whether they are a pro or non-pro, we contact the customer and ask them more pointed questions, or have them make changes. If someone calls themselves a trader on social media when they are not, we tell them to change it,” Lee says. This isn’t always a deliberate attempt to mislead brokers and exchanges—or a costly way to impress others on social media: “Sometimes, people have left a job but keep their old job title on LinkedIn, so they would be determined to be a professional.”
Beyond social media and LinkedIn, capturing the data to validate an individual’s status is a challenge in itself. D8A Force, for example, uses a database of residential addresses to verify whether the address of someone claiming to be “non-professional” is a home or business address, and databases of companies to be able to validate the employer information supplied by a client to see whether they work for a financial services firm. In addition, the vendor has created its own data sources, such as a database of roles and occupations that are “professional,” as well as patterns of activity that would suggest an individual is a professional rather than a purely retail investor.
None of this data is scraped from websites, Bhuvaneshwar says, adding that after founding the company, he spent six months wrangling data to ensure it was compliant. “Our lawyers review the terms of each data source we use to make sure we’re compliant. For the first few months, I probably spent more on lawyers to make sure the data—and the way in which we get the data—was compliant. I didn’t want to solve one compliance problem only to cause another.”
Ultimately, if a firm cannot satisfy itself that a client falls into the non-professional designation, it will treat them as a professional consumer of data.
It’s not just the criteria that a client must satisfy to be designated non-professional that are a challenge for brokers. Exacerbating the issue is the fact that criteria vary by exchange and region. To this point, exchanges are reported to be working on “fairer” models. For example, NYSE has proposals to introduce three new tiers of fees for the SIP consolidated tapes that better reflect usage and value of the data, rather than specifically determining who consumes it (on the assumption that certain classes of investors will require one type of data, while others can make do with a different level of data.
Still, while there are efforts taking root to address these challenges, the industry still lacks any standardization around these criteria that would make it easier for firms to accurately report to any exchange they might trade on.
“Different exchanges have different rules about who is professional or non-professional. While in Europe, they tend to look at how the data is used, and whether it’s your personal money being invested, the US has rules that if you work for a regulated firm, you’re a professional,” Lee says.
Where this becomes an issue, others say, is that you could work in any role at that regulated firm—from a senior executive who may or may not actually consume data as part of their job, to IT support staff who deal with data systems but don’t consume the data itself, to a janitor—and still be deemed a professional investor when trading your own account.
“At the definitions level, there is still dissatisfaction about some cases. For example, an executive assistant at a bank may be registered with regulators but does not trade as a professional—but if they answer an exchange’s questions correctly, they would probably be classified as a professional. And there has always been confusion when dealing with family trusts,” says FISD’s Davin.
However, Davin remains optimistic about the industry’s ability to solve the pro-versus-non-pro challenge. “I think there are a lot of opportunities to automate these compliance efforts for everybody’s benefit so that exchanges should be getting the correct amount of fees from the get-go, and brokerages don’t have any lingering compliance issues,” he says.
In the meantime, as the industry continues to work towards standards for defining pro-versus-non-pro, vendors like Prosparency and D8A Force can create something of a proxy standard. In June, Desjardins’ use of KYP was certified by Canadian exchange TMX Group under its audit program. “We want to get the KYP software certified by exchanges,” says MDMS president Scott Villa. “Then others can use it, and exchanges and the industry can be on the same page.”
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