Game Theory Optimized Fraud:
How the Unlawful Internet Gambling Enforcement Act Created a Virtually Risk-Free Environment for White Collar Crime in Online Poker
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In late 2002, the United States Court of Appeals for the Fifth Circuit held that because the Wire Act did not prohibit non-sports internet gambling, consumer debts that occurred in connection with that form of gambling did not equate to illegal proceeds for credit card issuers. Therefore, neither the federal mail fraud nor wire fraud statutes supported the plaintiffs’ civil RICO claim against the defendants, Mastercard International and Visa International, issuers of the credit cards the plaintiffs used to purchase virtual chips to gamble at online casinos. Almost a decade later, Congress addressed the gap in the Wire Act by passing the Unlawful Internet Gambling Enforcement Act (“UIGEA”), making it illegal for payment processors to process credit card payments and bank transfers to internet gambling websites in the United States. On April 15, 2011, the Department of Justice executed its only recorded enforcement of the UIGEA targeting online poker when it seized the websites, assets, and player deposits of several online poker companies operating within the United States. This article discusses how, in the aftermath of April 15, 2011, the UIGEA inadvertently created an environment for fraudsters to target U.S.-based players. The risk created by the UIGEA increased under the 2018 Supreme Court decision Murphy v. National Collegiate Athletic Association, which held that federal legislation prohibiting state authorization of sports gambling violates the United States Constitution’s anticommandeering rule. Murphy opened the door for states to permit certain online poker sites to accept funds from in-state residents, contrary to the UIGEA’s prohibition on payment processors sending and receiving transactions between banks and online poker operators. As a result, online poker is regaining popularity in the U.S. as states pass legislation authorizing select online poker sites to accept money transfers from residents. This article examines a 2020 online poker scandal to illustrate how the UIGEA exposes U.S.-based online poker players to white-collar criminal activity and how to amend the UIGEA to address this concern.
Bye, bye to poker online,
Drove my credit to the limit, now my bank account has run dry,
And to them good old days of winning with Ace-King high
Before 2011, April 1-5,
When DOJ brought an end to poker online.
Or did it?
Public corruption, money laundering, bank fraud, wire fraud, financial institution fraud, and fraud against the government are just some of the criminal activities that the Federal Bureau of Investigation (“FBI”) considers white-collar criminal activity. The common thread for these and other white-collar crimes is that those perpetrating the activities are financially motivated to obtain or avoid losing “‘money, property, or services[,] . . . or to secure a personal or business advantage.’” Many of these crimes now occur in a different arena: online poker.
In a 2002 report examining Uniform Crime Reporting (“UCR”) data, the FBI found that computer-based criminal offenses accounted for forty-two percent of white-collar offenses, with larceny-theft comprising the most significant proportion of those offenses. Twenty years later, vast improvements in technology provide tech-savvy fraudsters with the means to execute more complex schemes that are more efficient and harder to detect.
Improvements in technology contribute to a constantly evolving criminal theater that presents persistent challenges in white-collar crime prosecution. Many criminal statutes predate these technological changes, which may make the statutes appear ambiguous as to what activities they cover. In white-collar criminal cases, whether a crime occurred turns on a careful examination of fact and law. White-collar criminal cases where fraud is suspected often leave questions about whether the perpetrator acted with criminal intent.
Sometimes, the courts may be the root cause of ambiguity regarding the perpetrator’s intent. Writing for the Supreme Court of the United States, Justice Scalia remarked in Cheek v. United States that even though ignorance of the law is generally not a defense to a criminal charge, proving willfulness as an element of a crime “requires the Government to prove that the law imposed a duty on the defendant, that the defendant knew of this duty, and that [the defendant] voluntarily and intentionally violated that duty.” The Court also held that whether or not objectively reasonable, a good-faith misunderstanding of the law or a good-faith belief that one is not violating the law negates willfulness. The holding in Cheek suggests that one can be ignorant of the law and still be accountable for the crime, but no matter how ridiculous the excuse may be, truly believing that one is doing nothing wrong is enough to negate the notion that someone willfully broke the law.
Another ambiguity that presents a challenge for prosecuting white-collar crimes arises from how the statute defines the conduct it intends to address. In Dowling v. United States, the defendant, Paul Edmond Downey, was convicted of mail fraud, interstate transportation of stolen property, and conspiracy to transport stolen property interstate. In reversing Dowling’s convictions, the court noted that “assessing the reach of a federal criminal statute . . . [requires] pay[ing] close heed to language, legislative history, and purpose in order to strictly determine the scope of the conduct the enactment forbids.” When “‘interpreting a criminal statute that does not explicitly reach the conduct in question, [the Court is] reluctant to base an expansive reading on inferences drawn from subjective and variable ‘understandings.’” Because the language in the statute did not concern itself with interstate shipments of bootleg and pirated sound recordings, it did not “plainly and unmistakably” cover Dowling’s conduct.
While white-collar criminal investigations often put the cart before the horse—connecting a person to potentially criminal activity versus connecting criminal facts to the suspect—prosecuting white-collar criminal cases still requires proving beyond a reasonable doubt each element of the crime alleged. Doing so demands precision; when technology advances beyond what is clear in the text of the existing criminal statute, passing new laws or amending current laws to address the threat is the logical next step. New and untested criminal statutes may also create opportunities for criminals unforeseen by those who drafted the laws.
The following discussion explores how the Unlawful Internet Gambling Enforcement Act (“UIGEA”) created an environment that rewards bad actors who engage in white-collar criminal activities such as wire fraud, money laundering, and violations of the Racketeer Influenced and Corrupt Organizations Act (“RICO”), by providing an environment virtually free from the fear of prosecution. Section I explains important poker concepts relevant to this discussion. Section II discusses the UIGEA and how its passage ended online poker in the United States, culminating with the Department of Justice’s (“DOJ”) April 15, 2011 assault on online poker. The critical examination of the Black Friday case in Section III provides the backdrop for showing how the UIGEA allows online scammers to implement technology as an artifice to defraud unsuspecting online poker players. Now that several states have legalized online poker, Section IV reviews the legislation several states have enacted to address the inadequacies of the UIGEA and ultimately suggests ways to amend the UIGEA to better protect U.S.-based online poker players from fraud.
I. Poker Concepts
The origins of poker in the United States are somewhat of a mystery. Though some historians have dismissed the theory that poker originated from the Persian game As-Nas, several indicators counter that claim. As-Nas has a history along Europe’s trade routes and likely “inspire[ed] similar games like ‘Poque’ in France, ‘Pochen’ in Germany, and even ‘Poca’ in Ireland.” It is hard to dismiss the theory altogether, given such names and other commonalities, like being played with a 20-card deck. Though it may never be determined if As-Nas is poker’s origin story, the American version traces back to the early 1800s and the Mississippi riverboat routes. American poker transformed into a 52-card deck and five-card game along the Mississippi River during the Civil War. That version of poker, commonly called five-card stud, was the predominant poker game in the United States until the turn of the century when it transformed into what is known today as Texas Hold’em (“Hold’em”).
Robstown, Texas, may claim to be the birthplace of Hold’em, but the game did not see mainstream popularity in the United States until the 1960s in Las Vegas, Nevada. There, Hold’em’s popularity continued to rise when “no limit wagering”—the ability to bet as much money as the players had on the table in front of them—was introduced, and No-limit Hold’em (“NLHE”) was born. The no-limit betting style seemed to spur the introduction of another element to poker: “bluffing.” These elements—bluffing, no-limit betting, and the ability to drop bad hands—made poker unique to other card games because a player did not need the best hand to win, and they, in part, are why poker is considered to be a game that “rewards skillful play better than any other card game.”
Some may say that another mystery is whether poker is strictly gambling or a game of skill. There is a strong argument for classifying poker as a skills game, but to say that it does not involve an element of luck is inaccurate. Poker is arguably a game of problem-solving that forces the player to make decisions based on incomplete information, so it is fitting that some see poker as “a mathematical construct . . . [where] all parameters of the game are mathematically defined.” When viewed through a mathematical lens, “the science of the game begins with the relative expectancies” of each hand, of which there are many. While players don’t have to memorize all of the “possible 2,598,960 different five-card hands that may be dealt from a 52-card deck,” there is no escaping the fact that mathematical analysis drives decision-making in poker.
Two essential calculations drive profitable decision-making in poker: expected value and pot odds. The quality of the decision is measured in expected value (“EV”)—the average result of a given play if it were made hundreds (or even thousands) of times. Expected value is most easily understood by flipping a coin. The two sides to a coin, heads or tails, correlate to the probability that on any given flip, the coin will land “heads” side up 50% of the time and “tails” side up 50% of the time. If a person were to bet on a coin flip and receive one dollar for every time it landed on “heads” but lose a dollar every time it landed on “tails,” the EV of that calculation would look like this: EV equals Heads (percent chance x money wagered) minus Tails (percent chance x money wagered). At even odds and a dollar per bet, we can figure out the expected value of our bet by multiplying the outcome if it lands on heads (50% x $1.00) and then subtracting the outcome if it lands on tails (50% x $1.00). The EV of this bet is 0 ($0.50 (heads) - $0.50 (tails)). In the long run, the participant will never make or lose money in this game. However, if the parameters change and the participant now gets two dollars for every time the coin lands on heads and only loses a dollar each time it lands on tails, then the EV increases to 0.50. In this new version of the game, the EV of the bet over the long run is $0.50 every time the bet is made. Poker players, and gamblers alike, consider this payoff to be a “positive EV” scenario.
The EV of a decision, derived from a related concept called pot odds, drives decision-making for poker players. Pot odds represent the relationship between how much a player stands to win in a hand of poker and how much the player must risk to get it — that is, the ratio between reward and risk. Matthew Hunt, a professional poker player and instructor at Solve For Why Academy, explains that understanding these concepts is important to be profitable at poker because:
[p]oker players should enter the pot with the goal being to make the highest expected value decision at each opportunity. Generally, the best way to accomplish this is to focus on giving our opponents the most difficult decisions possible by using our understanding of the pot odds model to capture more than our fair share of each pot.
Consistently making the highest expected value decisions that yield the most profit over the long term is easier said than done. Fundamental to “the game of poker is [the] ability to [consistently] make good decisions based on that incomplete information,” which “mak[es] gaining and using information each a skill unto themselves.”
How these concepts translate to winning more than our fair share of the pot is less of a mystery. Folklore has long consisted of this notion that poker players look for “tells:” something a player does that may give a clue about how they feel about their cards or some other action during a poker hand. Poker tells can include anything from a change in breathing pattern or the tremble of a hand to the betting pattern the player makes when holding a good hand or bluffing. Many consider physical tells to be unreliable because they are generally specific to the person, so what may be a sign of a bad hand for one player could be how a different player acts when they have a good hand. In reality, especially in online poker, players may incorporate a mathematically driven approach instead, such as a Game Theory Optimal (“GTO”) strategy.
GTO poker is a strategy that applies universally; it is “essentially a defensive strategy that incorporates the principles of balanced [hand] ranges” designed to be unexploitable and to maximize profit by making it more difficult for an opponent to pick up on betting patterns. GTO strategies are a function of the range of hands “that you or your opponent could logically have in that specific situation.” A balanced range is “a range of hands that is unexploitable by virtue of a varied playing strategy.” The idea of incorporating a balanced range is to avoid exploitation by your opponents because it is obvious, for example, that every time you bet, you have a strong (winning) hand.
One way in which players can balance their play is by incorporating a mixed strategy where the player may bet, check, or raise a certain percentage of the time. By doing so, a player cannot accurately predict an opponent’s actions in certain situations. Players who implement a GTO strategy may adjust the frequency (i.e., randomization) of how often they take a certain action to disguise their play more effectively. The sheer number of decisions and possible ways to play a poker hand brings a level of complexity at the GTO level that may be why poker great Mike Sexton used to say,“[No Limit] Hold ’Em: the game that takes a minute to learn but a lifetime to master.” GTO poker dictates that “player[s] [are] always making the decision that returns the most profit in the long run.” Because humans are prone to making mistakes, getting tired, and acting on emotion, it is almost impossible for a human player to implement the right decision in a game where there are “10161 (one followed by 161 zeroes) situations, or information sets, that a player may face—vastly more than all of the atoms in the universe,” let alone fully randomize their actions. Fortunately, “there’s an app for that!”
 Words by Jeffrey Woolf, inspired by and sung to the melody of, American Pie by Don McClean.
 What We Investigate, Fed. Bureau of Investigations, https://www.fbi.gov/investigate/white-collar-crime (last visited June 7, 2023).
 Cynthia Barnett, Dep’t of Just., Fed. Bureau of Investigations, The Measurement of White-Collar Crime Using Uniform Crime Reporting (UCR) Data 1 (Nov. 10, 2000) (quoting Fed. Bureau of Investigations, Dep’t of Just., White Collar Crime: A Report to the Public 3 (Gov’t Prtg. Off. 1989)), https://ucr.fbi.gov/nibrs/ nibrs_wcc.pdf.
 Id. at 4.
 Fang Yu, How Is Digital Fraud Becoming More Sophisticated As Technology Advances, Forbes (Sept. 15, 2019), https://www.forbes.com/sites/quora/2019/09/16/how-is-digital-fraud-becoming-more-sophisticated-as-technology-advances/?sh=7ac5a3ee4e84.
 Jerold H. Israel et al., White Collar Crime Law and Practice 91 (4th ed. 2015) (quoting Kenneth Mann, Defending White Collar Crime – A Portrait of Attorneys at Work 114–15 (1985)).
 498 U.S. 192 (1991).
 Id. at 201.
 Id. at 192.
 Id. at 202.
 Jerod H. Israel et al., supra note 6, at 91.
 473 U.S. 207 (1985).
 Id. at 208–09.
 Id. at 213.
 Id. at 218 (quoting Williams v. United States, 458 U.S. 279, 286 (1982)).
 Id. at 228.
 See United States v. Gotti, 457 F. Supp. 2d 403, 405 (S.D.N.Y. 2006).
 31 U.S.C. §§ 5361–5366 (2021).
 The Mysterious Origins of Texas Hold’em, TXK Today (Sept. 22, 2020), https://txktoday.com/technology/the-mysterious-origins-of-texas-holdem/.
 H.R. Con. Res. 109, 2007 Leg., 80(R) Sess. (TX. 2007).
 The Mysterious Origins of Texas Hold’em, supra note 21.
 No Limit Hold’em is a variant of poker that involves potentially four rounds of betting per hand: preflop is the round that starts when the dealer deals two “down cards’ to each player who then decides if they will fold, call or raise the big blind (BB)—which is the larger of the two forced bets, the small blind (SB) being the other. If at least one player calls the previous bet, then the second round—the flop round—begins. The flop is when the dealer will put out three cards that all players in the hand can use to make the best five-card hand. Another round of betting takes place and then the process repeats on the “turn card” round and then continues to the “river card” round. Until the last bet is called or there is fold, the hand continues. At any time on any round, a player can move “all-in” because there is “no limit” to how many of the chips a player can use to bet. A player can be all-in preflop, on the flop, on the turn, or after the river. It is entirely up to the player. See generally How to Play No Limit Holdem, TopPokerSites.com, https://www. toppokersites.com/ poker-rules/ no-limit-holdem/ (last visited June 7, 2023).
 Poker, Britannica, https://www.britannica.com/topic/poker-card-game/Skillful-play#ref97575 (last visited June 7, 2023).
 Is Poker a Game of Skill or Just Luck? The Debate is Over, Upswing Poker (Sept. 2021), https:// upswingpoker.com/poker-skill-luck-debate-over/ (last visited June 7, 2023).
 ACADEMY PRIMER 2.0 with Matt Hunt: The Pott Odds Model, Episode 2, Solve For Why TV, https://solveforwhy.io/programs/academy-primer-2?cid=2964369&permalink=academy-primer-2-ep2&offset=180 (last visited June 7, 2023).
 Poker, supra note 32.
 What is Expected Value (EV) in Poker? This Basketball Analogy Will Make it Clear, Upswing Poker, https://upswingpoker.com/expected-value-ev-poker/#what (last visted June 13, 2023).
 The Essential Guide to Poker Math, Slowplay, https://www.slowplay.store/blogs/news/poker-math (last visited June 13, 2023).
 Learn Poker: What Are Pot Odds? How to Calculate Pot Odds and Winning Odds, MasterClass (Sept. 29, 2021), https://www.masterclass.com/articles/learn-poker-what-are-pot-odds.
 10 Hold‘em Tips: Pot Odds Basics, Poker News (July 18, 2016), https://www.pokernews.com/ strategy/10-hold-em-tips-04-25360.htm.
 Matthew Hunt, LinkedIn, https://www.linkedin.com/in/matthew-hunt-a2a5151b1/ (last visited June 7, 2023).
 Text message from Matthew Hunt, Instructor, Solve For Why, to Jeff Woolf, Law Clerk to the Honorable Anne K. Albright, App. Ct. of Md. (Feb. 11, 2022, 07:13 pm EST) (on file with author).
 THE PRIMER COURSE: Laying the Theoretical Groundwork with Matt Hunt, Episode 1. Afforded Information & Key Terminologies, Solve For Why TV (Apr. 3, 2022), https://solveforwhy.io/programs/the-primer-course?cid= 1979761&permalink=primer-ep1&offset=141.
 Matthew Pitt & Christina Bradfield, Poker Face Meaning – What Is a Poker Face and How to Get a Good One? Poker News (Oct. 31, 2022), https://www.pokernews.com/strategy/what-is-a-poker-face-and-how-to-get-a-good-one-42232.htm.
 Blake Eastman, Most Advice on Poker Tells is So Basic It’s Incorrect, Beyond Tells, https://www. beyondtells.com/pokerresearch/most-advice-on-poker-tells-is-so-basic-its-incorrect (last visited May 30, 2023).
 Learn About Poker: What is GTO (Game Theory Optimal), MasterClass (Sep. 3, 2021), https://www. masterclass.com/articles/learn-about-poker-what-is-gto-game-theory-optimal.
 GTO Wizard, Principles of GTO, Gto Wizard (Dec. 2022), https://blog.gtowizard.com/principles -of-gto/ (last visited June 14, 2023).
 Poker wit and wisdom: five more useful sayings, Paul Phua Poker, https://paulphuapoker.com/ poker-wit-wisdom-10-useful-sayings-pt-2/ (last visited June 7, 2023).
 Game Theory Optimal (GTO) Texas Holdem Poker Theory, Cornell Univ. (Nov. 3, 2021), https://blogs. cornell.edu/info2040/2021/11/03/game-theory-optimal-gto-texas-holdem-poker-theory/.
 Ken Walters, Brains Vs. Artificial Intelligence: Carnegie Mellon Computer Faces Poker Pros In Epic No-Limit Texas Hold’ Em Competition, Carnegie Mellon Univ. (Apr. 24, 2015), https://www.cmu.edu/news/stories/ archives/2015/april/computer-faces-poker-pros.html.
 THERE’S AN APP FOR THAT, Registration No. 3,884,408 (referring to Apple’s slogan in relation to its retail store for purchasing and downloading applications (“apps”) to one’s iPad, iPhone, or iPod).