Computing devices, miniature cameras, and artificial intelligence models — those looking to hit the jackpot are constantly expanding their digital arsenal, and casinos have to stay one step ahead of the game. Platforms like Richard Casino illustrate how modern gambling venues are not just keeping up but actively evolving, offering players both thrilling gameplay and robust security to prevent exploitation.
The famous blackjack theorist Edward Thorpe wrote: “I decided to go to Nevada partly to shut up the fans of the common and rather annoying taunt against scientists: ‘If you are so clever, why are you so poor?’” In the early 1960s, using an IBM 704 computer and probability theory, the mathematician developed card-counting schemes to determine a player’s chances of winning — and successfully tested them in Reno and Las Vegas casinos with the support of bookmaker and gambler Emmanuel Kimmel.
Later, Thorpe and Claude Shannon developed a pocket-sized roulette computer, controlled by shoe-embedded switches and transmitting signals via tiny headphones. This device increased a player’s odds by up to 44%, though its complex setup made it hard to conceal, and fragile wires often broke. After Thorpe published his best-selling book Beat the Dealer in 1962, a wave of new players took his methods to the tables — often outperforming the master himself.
Hack the programme
Offense:
In the 1980s, MIT students formed a now-legendary blackjack team that used card counting and mathematical strategies to beat casinos for over a decade. By 1985, Nevada responded by banning wearable computers in gambling venues. Around the same time, physics student Bill Benter created a program to predict horse race outcomes using factors like rest periods, weather, and diet. His system made him nearly a billion dollars on Hong Kong racetracks.
In the 1990s, Dennis Nikrash exploited slot machines using chips he acquired from a leading manufacturer. His team would distract staff while he discreetly opened machines to manipulate their systems, often walking away with massive jackpots. Another infamous figure, Tommy Glenn Carmichael, spent decades rigging slots using inventive tools — including a device called the Light Wand, which disrupted optical sensors to trigger incorrect payouts.
Defence:
As early as 2002, cameras and facial recognition systems appeared in casinos. Images of people suspected of cheating were entered into a database, and the system transmitted the photos to other casinos by email.
Today, Las Vegas gaming establishments are incubators of the world’s most advanced surveillance technology. Cameras capture every movement of visitors, and software profiles players, collecting data on winnings and gaming strategies. In 2010, Mirage equipped its baccarat tables with a system known as Angel Eye. A scanner hidden in the shoe – the plastic case from which the cards are dealt – reads invisible barcode strips on the cards and prevents the cards from being switched with counterfeit ones. The TableEye21 system uses ceiling-mounted video cameras and software to analyse the video and can track information from casino chips that have radio frequency (RFID) transmitters embedded in them.
Wait and watch
Attack:
In the early noughties, three players – Niko Tosha, Nenad Marjanovic and Livia Pilisi – won £1.3m by guessing roulette results at London’s Ritz Club casino. The trio’s coordinated behaviour attracted the attention of the establishment’s staff, and the ‘lucky’ players were soon detained on suspicion of cheating. No computing equipment police did not find, and the casino had to pay the group’s winnings, but the security service for many years continued to conduct its own investigation. And only after a dozen years it turned out that the players did not need computers to predict the outcome of the game. The wheel at the Ritz Club was very old and worn: from time to time it was slightly tilted, and the ball constantly stopped on the same site.
For the record
Planned Randomness. Computers are logical devices, and randomness is contrary to their nature. External sources of randomness can be physical phenomena – for example, thermal noise in computer systems, but converting them into numbers is long and difficult. Therefore, computers generate pseudo-random numbers that are almost uniformly distributed and almost independent of each other, but obtained not by true randomness, but by a given algorithm.
Defence:
After the secret of the lucky trio was revealed, many casinos around the world had to upgrade their roulette wheels in 2005. Following the recommendations of the British Gambling Commission, casinos equipped the wheels with laser sensors and devices that measure tilt, as well as dividers and grooves that change the trajectory of the ball. Entire analytical departments monitor the roulette wheel in real time and check whether certain sectors win more often than statistical models dictate. And special randomiser programmes change the speed of roulette rotation randomly. True, casinos are still not immune to fraud: those who bet online can calculate the speed and trajectory of the ball on the video broadcast with the help of a computer programme.
Find the non-random in the random
Attack:
Computer programmer Ronald Dale Harris reconfigured slot machines in the 1990s so that large winnings could be obtained by inserting coins of a certain denomination in a specific sequence. He also developed a programme that determined which numbers the pseudo-random number generator would choose in the Keno numerical lottery. Harris benefited from his experience working for the Nevada Gaming Control Board: he looked for vulnerabilities in the software of the machines and had access to the source code.
In 2011, seven Chinese nationals installed miniature cameras and mirrors in baccarat card shuffling devices at casinos in the Macau area. The cameras transmitted video to an analyst who determined the order in which the cards were shuffled and sent instructions to players’ mobile phones – all of which helped the group earn $3 million.
Protection:
Vulnerabilities were found regularly; back in 2002, after a similar case, an unnamed manufacturer consulted mathematician and former card magician Percy Diaconis. Diaconis studied card shuffling using an analogy to Markov chains*, which is a sequence of events or actions whose outcome depends only on the current state and not on how that state was reached. He showed that after seven shuffles of the deck, the probability of a random card falling out increases dramatically. The company’s machines did not fulfil these criteria: the sequence in the deck after shuffling remained predictable, and as a result the next model had to be rejected.
Use ‘reverse engineering’
Attack:
In 2009, after slot machines were banned in Russia, a St. Petersburg programmer known under the pseudonym Alex purchased decommissioned equipment and found vulnerabilities in pseudo-random number generators. When generating ‘random’ pictures, the machines relied on certain algorithms: some initial value was added to other values.
Alex’s accomplices filmed the work of slot machines in casinos around the world on their phones and sent the video to experts. After analysing the file, the experts uploaded timestamps to the app. At the right time, the phone vibrated – the player immediately pressed the machine’s button and won. In a week, a group of four people earned about $250,000. In 2014, one such group was detained – the manipulations with the phone were too noticeable. Alex himself in 2016 decided to quit the ‘business’, and lastly to blackmail the manufacturer of slot machines – the company Aristocrat. For a generous reward, he promised to tell them how to fix the vulnerabilities. The company refused the offer – and later journalists discovered that the algorithms of its Helix machines, provided by Alex, are mentioned in Donald Knuth’s classic monograph ‘The Art of Programming’, which has been published since 1968.
Protection:
Various regulators and independent laboratories issue industry standards and guidelines for slot machine manufacturers that mandate the use of complex algorithms. Independent laboratories test pseudo-random number generators and certify only those that guarantee players truly randomised game results. For example, Gaming Laboratories International specifies what types of attacks gaming systems must be resistant to. Specifically, the manufacturer must ensure that the generators do not have the same initial value, that subsequent values cannot be predicted knowing the previous ones, and that the algorithm cannot be hacked by a skilled attacker who may know the source code.
Rely on artificial intelligence
Attack:
In 2021, Craig Smith, a former New York Times correspondent and machine learning researcher, described the capabilities of Akkio, an artificial intelligence platform. Its co-founder John Reilly fed the system data on horses scheduled to race in the coming weeks to predict the probability of winning for each. The model created by the platform successfully calculated the winner in six races out of ten.
Protection:
Establishments have started to use machine learning models to simulate fraudulent behaviour. They can identify, for example, unusual betting patterns, card counting or collusion between players, and create predictive models to assess the likelihood of fraudulent behaviour.
In addition, machine learning makes it possible to profile players by analysing their behaviour and preferences and create tailored offers for them. This personalisation makes more players engage more – and lose more, according to the famous statistical problem of player ruin. It states that if a player with limited funds continues to bet against an opponent with unlimited funds – that is, against a casino or other betting players – he will eventually lose everything.
