How AI Is Changing Fraud Detection and Personalization in Online Gambling Apps

Online betting apps look simple from the outside: pick an odds market, place a bet, watch it settle. Underneath that simple interface, two very different AI systems are running constantly, one trying to keep fraudsters out, and one trying to keep you playing.

Both have improved massively in the past few years, and understanding them both helps explain why they feel the way they do.

Why Fraud Detection Had to Get Smarter

Banks and payment processors consider gambling apps to be high-risk because of chargebacks and the rate at which funds flow. Whereas in a retail transaction, fraud could occur days later, a payout in betting is often immediate, and if the bettor withdraws the money before anyone is aware, it’s likely that the money is gone.

Older rule-based fraud systems relied on thresholds—anything over a certain amount, a list of known bad IP addresses, and so on. That doesn’t work when people get more inventive with their use of the bonus, such as one player opening multiple accounts and using the same welcome bonus over and over again, or a group of players coordinating a group play to transfer the bonus winnings to a specific account.

In contrast, modern AI models work differently. They don’t rely on a set of rules; instead, they create a baseline of behavior for each user and score incoming activity against that baseline. When someone suddenly starts making massive bets across all their accounts or logs in from a new device and location within hours, the system assigns a real-time risk score, and additional verification may be required before the transaction is complete. The same method applies to anti-money laundering (AML) screening, where the fund source of a user and their wallet transactions are compared to watchlists, and age verification, which involves comparing ID documents submitted in the onboarding process with facial recognition data.

The Other Side: Personalization Built to Keep You Engaged

The second type of AI behind these applications focuses on engagement, not security, and its impact on user behavior is now being assessed directly. Academic research has been conducted that examines the user behavior of a real money site on bet level data and compares this to the behavior of these users prior to this type of personalization becoming commonplace in the industry in 2016, with their behavior in 2021 when it was commonplace. The researchers found that receiving a personalized bonus was related to lower individual wagers overall, while being on a profit run or cashing out early was linked to larger wagers and more consistent playing, which the researchers suggest fits with algorithms that support the pre-existing psychological tendencies such as loss aversion and the illusion of control.

A series of researchers that can discern when a user is close to logging off, or is in a losing streak, can time a bonus offer so that it arrives right when the user is ready for it. These offers can differ from one customer to the next, so they are not the same type of ‘one size fits all’ bonuses that were more common before, and that is why regulators are starting to take a more serious look at their design.

Regulators Are Already Responding

This isn’t a hypothetical concern for policymakers; it’s an active regulatory front. The UK Gambling Commission has implemented new regulations that limit how operators can offer bonuses and incentives, such as stopping operators from offering a combination of different types of product in a single “bonus or incentive” offer, to eliminate the incentives for operators to push customers towards riskier or more intense gambling.

At the EU level, the European Commission’s AI Act takes a broader approach, treating AI systems used for profiling individuals as high risk by default, which is directly relevant to any gambling platform using behavioral data to personalize what a user sees or is offered. Gambling-specific legal analysis has pointed to the Act’s prohibition on certain practices as directly addressing the ability of AI to adjust the payments or bonuses in response to it identifying a weakness the player has.

Two Systems, One Underlying Skill Set

Technically, both fraud detection and personalization require the same skill: reading a lot of real-time user activity and automatically making a decision based on that data. The difference lies solely in what each system optimizes for: one is geared towards preventing money from going out the door of the platform, while the other is geared towards ensuring a good-faith user stays engaged for as long as possible.

For app developers, this dual use of behavioral AI also raises a practical business question that has nothing to do with gambling ethics: how a gambling operator generates the iGaming traffic needed to make either system worth building in the first place, since neither fraud models nor personalization engines have enough data to work well without a meaningful base of active users to learn from.