According to a report, the online gaming industry, as of 2020, is a $159+ bn market, with an observed growth rate of approximately 9.3% year on year, with close to $60 bn attributed to online gambling.

The scale of Fraud in Online Gambling

Online gambling refers to any kind of gambling conducted on the internet. This includes the likes of virtual poker, casinos, sports betting etc. Online casinos alone drive about 11% of global internet traffic, and the numbers are growing at a much faster rate, given the advent of the COVID-19 pandemic, which is a big setback for the offline casino experience.

Source: Newzoo Global Games Market Report 2020

The multi-billion-dollar online gaming market inherently attracts fraudsters at different levels and scales, naturally becoming one of the biggest sources of direct as well as indirect revenue and opportunity losses for online gaming companies. According to a recent research report:

  • 33% of paying gamers agree that they are less likely to spend on online games or make in-game purchases due to the risk of online payment frauds
  • 19% of paying gamers have experienced fraud when paying for games
  • 56% of paying gamers purchase, sell, or trade in-game items outside of a game; 1/3rd of these have experienced fraud while doing this

Challenges in Fraud Detection

Traditional methods to combat fraud include methods such as IP/device tracking, geo-location-based tagging, multi-level KYCs, OTP/fingerprint-based verifications, and other rule-based mechanisms. While these methods have had a great deal of success in the past, they fall short in more ways than one, considering the evolving caliber of online fraudsters and increased expectations from online consumers. Some of the challenges include:

  • The changing landscape of fraud – Rules don’t suffice to identify new and emerging fraud types. AI systems can help deal with this variability
  • Operations cost and speed – Companies incur huge costs in terms of manual verification processes. Intelligent AI systems can offer robust verification methods that can help reduce/augment the manual overhead as well as speed up the verification cycles
  • Gaming experience – Increased security measures add friction for players. According to a recent research report, 44% of paying gamers online in the US agreed that encountering additional security measures while making in-game purchases negatively impacts their gaming experience and makes them less likely to pay
  • False Positives – Rule-based systems can drive out a lot of genuine customers by flagging them as fraudsters, causing legitimate players to have a bad experience. This eventually results in lower customer retention

Opportunities for AI to help tackle various fraud types

We now scope out predominant fraud types that occur in an online gambling setting, evaluate the impact of these frauds, and identify how AI can help in each of these verticals.

  • Account Takeover: Account takeover is a type of identity theft fraud. Hackers usually target high-profile gamers who have acquired special strengths in the game or have excellent credentials with loads of gaming currency. The accounts are then sold to rather amateur players to avoid raising any suspicions. The impact of account takeover are as follows:
    • Loss of trust and/or reputation
    • Legal implications of data breach
    • Loss of gaming assets/currency for user

Behavioral Analytics can be used to tackle this type of fraud. AI systems can be built for user profiling for each user; any anti-pattern in behavior after login can be flagged as a fraud login. Also, once identified, payments/transactions can be blocked until a detailed review happens.

  • Bonus Abuse: Bonus abuse is a form of collusion. Online casinos often offer various bonuses to maintain and attract customers. Sometimes players are gifted a free bonus, while in other cases, players are rewarded by matching a good fraction on their initial deposit. Players take advantage of this by signing in through multiple accounts to get multiple bonuses. They then collude in order to bet the amount a certain number of times before being able to withdraw it. The impact of bonus abuse are as follows:
    • Loss of revenue
    • Zero retention users entering the system

Game Analytics can help reduce such forms of collusion. AI systems can be built to identify intentional losing patterns.
Surge detection systems can also be built using AI. They monitor traffic and flag surges based on spatio-temporal attributes to prevent bot invasions.

  • Chip Dumping: Chip dumping is a form of collusion usually used to launder money or pay for black market services. Gambling winnings are considered as legal earnings, hence multiple people collaborate to lose to a single person who then encashes all the earnings. The impacts of chip dumping are as follows:
    • Loss of revenue
    • Money laundering

Game Analytics can help reduce chip dumping. AI systems can be built to learn gaming patterns of regular users vs. users who are colluding.
Account linking systems can also be built which attempts to link accounts that are frequently observed on the same gambling table.

  • Chargebacks: Chargebacks fall under the category of payment frauds. This could happen due to credit card thefts. The legitimate owner raises a complaint with their concerned financial institution to report an unauthorized payment. If the institute reverses the charges, then the casino to which the unauthorized transaction was made becomes responsible for the amount charged. Fast payout casinos suffer the most. Also, while casinos can fight these charges, they refrain from doing so in order to maintain relationships with the financial institutions.
    The impact of chargebacks are as follows:
    • Loss of revenue
    • Relationship with financial institutions

Behavioral Analytics can be used to analyze purchase patterns of users and flag unusual purchases.
Credit Card Fraud detection systems can be built using historic transaction patterns and attributes of fraudulent transactions to detect if a transaction was fraudulent or not.
Collaborative scoring is another AI-based technique that can be employed to understand if other users at the same stage in the game make similar purchases or not.


While fraud has been a pressing issue in the gaming industry for decades, newer challenges emerge as more players move to digital gambling platforms. This opens a wide horizon for organizations to adopt AI to combat the changing landscape of fraud.

The opportunities for AI in this domain seem very promising. However, when it comes to adopting these AI practices, organizations still have a long journey to walk in terms of moving towards a “Data First” mindset. The way they go about data strategy, storage and computing infrastructure, ingestion pipelines, as well the adoption of newer big data technologies in contrast to legacy systems would determine how fruitful these AI endeavors would turn out to be.


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Nitesh Kumar

Posted by Nitesh Kumar

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