Name Matching Helps Protect the Sports Betting Industry Against Money Laundering

The Sports Betting Industry Needs to Comply with AML Laws

In May 2018 the U.S. Supreme Court decided that states could allow betting on sports. Its decision also stipulated that they needed to regulate the activity. Since then, more than 30 states have legalized some form of sports betting and others are debating to follow suit.

Unfortunately for state governments that see a new lucrative source of revenue as well as individuals and organizations that are interested in exploiting the wider opportunities created by the Supreme Court decision, sports betting has traditionally been associated with illegal activities of many kinds. In particular, individuals and organizations interested in laundering tainted money often use sports betting organizations, whether physical such as casinos or on-line betting sites, as a convenient place to sanitize it. It’s simple enough to open a betting account and deposit a large sum in it. One scenario, perhaps the simplest, is that of a customer opening an account at an on-line betting site. They can then deposit amounts into that account from different sources, including even anonymous ones such as pre-paid cards. After a while they simply request the funds back from the account without even needing to engage in any betting activity. They receive the funds which now have come from a legitimate business and can be justified as the result of legitimate betting activity.

Organizations interested in participating in sports betting need to realize that they are required to comply with the Anti-Money Laundering regulations and need to establish programs that effectively deter money laundering.

What Sports Betting Establishments Need to Do

A fundamental part of complying with anti-AML regulations is investigating all customers. A sports betting establishment needs to perform due diligence checks on them, which in this context means screening against various sanctions lists. These include ones provided by the U.S. Treasury Department’s Office of Foreign Assets Control (OFAC), the UN Security Council, or the European Union. This task would be highly onerous for most companies if done by hand. Luckily, there’s a technology that can help speed things up: Name Matching.

How Name Matching Uncovers Illicit Betters

Name Matching is an AI technology that determines if two names are a likely match or not. Consider the following examples of the challenges that have to be met:

• Names can vary: “James Miller” vs. “J. Miller” vs. “James S. Miller”
• Spelling variants, nicknames, and simple typos may be common in the data: “Elliott” vs. “Eliott” vs. “Elliot” vs. “Eliot”
• Names of different ethnicities have their own idiosyncrasies:
  • Spanish names contain both patronymic and matronymic surnames, but the latter is often dropped: “Juan Ramirez Meseguer” vs. “Juan Ramirez”
  • Arabic names, which commonly contain an element “al-,” may appear with or without it, as in “Musa al-Iqbal” vs. “Musa Iqbal”
• Items such as dates need to be handled appropriately. The ordering of the pieces can vary:
  • “January 1, 2017” vs. “1 January, 2017”
  • “10/01/2016” vs. “01/10/2016” (U.S. vs. European)
  • “July 3, 1955” is obviously a pretty close match to “July 3, 1956,” but “August 3, 1964” could also be a close match to “August 3, 1974.” The apparent ten-year gap could be the result of a simple typo involving one digit.
• Addresses offer some complex matching challenges:
  • “75 9^th Street, Albany, NY” vs. “75 Ninth St. Albany, New York.” Here there are three differences that need to be handled.

How Name Matching Works

Name Matching, aka Entity Matching, is a technology that intelligently matches entity records that represent the same identity. It matches each individual field and provides a score for likelihood of identity. It combines the individual field scores into a unified score. Business rules can then determine the appropriate threshold score and whether borderline cases should be sent to human reviewers.

In sum, Name Matching provides an effective means of meeting AML requirements. Organizations planning to enter or expand into sports betting will need to incorporate Name Matching as part of their overall strategy.