A new account doesn’t always mean a new player. For iGaming operators, that distinction can be the difference between a successful promotion and a bonus program quietly being exploited by the same fraudster over and over again.
A player can use a different email address, phone number, identity, IP address, or even a seemingly different device environment to look like someone new. But underneath those changes, the same device, tools, or fraud network may still be connecting the activity.
That is why effective iGaming bonus abuse prevention needs to look beyond the account itself.
Why bonus abuse is more than just a “free bonus” problem
Bonuses are designed to attract genuine players. Welcome offers, free spins, referral rewards, deposit matches, and limited-time promotions all give players a reason to sign up and stay engaged.
The problem starts when fraudsters find ways to collect those incentives repeatedly.
The simplest example is multi-accounting: one person creates several accounts, claims a new-player bonus on each, and either withdraws the rewards or uses the accounts to gain an unfair advantage.
But the problem can become much more organized.
Fraudsters can use app cloners, emulators, VPNs, automation, device spoofing, or other malicious tools to create and operate multiple accounts at scale. SHIELD's iGaming material specifically identifies multi-accounting as a gateway to bonus abuse and collusion, with these tools helping fraudsters create multiple fake players.
So the real question for an operator isn't simply:
“Does this account look legitimate?”
It is:
“What is this account connected to?”
Multi-accounting makes a legitimate player look like many
Imagine a promotion offers a welcome bonus to every new player.
A genuine player signs up once, deposits, plays, and eventually moves on.
A fraudster sees something different: an opportunity to repeat it.
They create Account A, claim the bonus, then create Account B with different credentials. If the operator only checks whether Account B has a different identity or email, it may pass as a new customer.
Now imagine this happening hundreds or thousands of times.
This is where multi-accounting detection needs more than identity matching.
The identity may change. The device doesn't necessarily disappear.
Device intelligence gives operators another way to connect the dots.
Instead of looking at each account independently, operators can examine whether seemingly unrelated accounts are associated with the same physical device or suspicious device environment.
That can expose patterns such as:
- Multiple accounts repeatedly originating from one device
- Devices associated with unusually high numbers of new players
- Emulators or app cloners being used to create accounts
- VPNs or proxies being used alongside account creation
- Device tampering or suspicious resets
- Multiple accounts showing coordinated activity
This is particularly useful because the device sits underneath many of the identities and accounts a fraudster creates.
Why traditional checks can leave gaps
KYC and identity verification remain important, particularly for regulated iGaming operators. But they answer a slightly different question:
“Is this identity valid?”
Bonus abuse prevention often needs to answer another:
“Have we seen this player, or the environment behind this player, before?”
A fraudster may have different credentials across accounts. They may even use different identities.
That doesn't necessarily mean the underlying activity is unrelated.
This is why device intelligence can complement identity verification for online casinos rather than replace it. Identity verification can establish who a player claims to be, while device intelligence can provide additional context about the device and environment being used.
The two approaches solve different parts of the problem.
What should operators look for?
Effective bonus abuse detection doesn't have to mean putting every player through additional verification.
In fact, excessive friction can hurt the very players an operator is trying to attract.
A better approach is to identify stronger signals of risk and apply additional scrutiny where it makes sense.
1. Look for unusual account-to-device relationships
One player using one device is normal.
One device repeatedly associated with large numbers of supposedly unrelated new players deserves a closer look.
For example, SHIELD's iGaming materials describe a fraud case where the device-to-player ratio averaged 1:10, with one cluster containing 85 users linked to a single device. The same case also showed shared sessions and screen-sharing activity used to coordinate play.
The value isn't simply in knowing that 85 accounts exist.
It's in understanding why they are connected.
2. Watch the device environment, not just the device
A device connection is one signal. The environment around it can add much more context.
Signals such as emulators, app cloners, VPNs, GPS spoofers, screen sharing, hooking, app tampering, or suspicious factory resets can indicate attempts to manipulate the environment or conceal activity. SHIELD Fraud Intelligence is designed to continuously profile device sessions and surface these types of real-time signals.
That can help operators distinguish between:
“This player looks unusual.”
and
“This player is using an environment commonly associated with abuse.”
That is a much more actionable distinction.
3. Connect signals across the player journey
Fraud doesn't necessarily stop once an account is created.
A suspicious player might:
Sign up → claim a bonus → create additional accounts → play → deposit → withdraw
Looking at these events separately can make the pattern harder to see.
Connecting device, account, behavioral, and transaction signals can provide a more complete picture of the player journey.
This is also where AI-powered fraud detection can help: rather than relying only on fixed rules, AI and machine learning can process large volumes of signals and identify relationships or patterns that may be difficult to spot manually. SHIELD's product messaging describes its platform as using AI, ML, LLMs, and deep learning alongside device intelligence to analyze large volumes of fraud signals.
Where device intelligence fits into iGaming bonus abuse prevention
The strongest approach isn't device intelligence versus KYC, behavioral analytics, or transaction monitoring.
It's about giving those systems better context.
Think about a new account that passes basic identity checks.
On its own, it might look fine.
Now add the device layer:
- The same device has already been associated with several accounts.
- An emulator is running.
- An app cloner is present.
- The device is masking its environment.
- Several accounts are appearing within a short period.
Suddenly, the account tells a very different story.
That's the advantage of device intelligence for iGaming: it helps operators move from looking at isolated accounts to understanding the connections between them.
A real-world example: seeing the fraud network behind the accounts
Pipa Studios' experience with Praia Bingo is a useful illustration.
The social gaming platform was dealing with fake accounts and multi-accounting, with fraudsters using tools such as app cloners and emulators to create accounts at scale and repeatedly access bonuses and rewards. These accounts could also be used to enter games as multiple players, giving fraudsters an unfair advantage.
Pipa Studios used SHIELD Device ID to identify devices associated with fake accounts, including when fraudsters attempted to spoof device parameters or perform factory resets. SHIELD Fraud Intelligence then continuously profiled device sessions and surfaced signals associated with tools such as hooking, app tampering, emulators, and app cloners.
The result was a 96% reduction in devices with fake accounts, along with a decrease in bonus and reward abuse.
The important takeaway isn't simply that one technology solved the problem.
It's that the operator gained visibility into the thing connecting the accounts in the first place: the device.
The goal isn't to block more players. It's to understand them better.
For operators, this distinction matters.
The objective of bonus abuse prevention shouldn't be to make onboarding harder for everyone. Genuine players should still be able to sign up, claim legitimate promotions, and enjoy the platform without unnecessary friction.
The goal is to identify the activity that doesn't make sense.
A single account may look perfectly normal.
Ten accounts might also look normal when viewed individually.
But if all ten connect back to the same suspicious device environment, the picture changes.
That's where modern online casino fraud detection needs to evolve: from checking whether individual accounts look legitimate to understanding the relationships between accounts, devices, environments, and behavior.
Device intelligence doesn't replace the other layers of fraud prevention. It gives them another piece of the puzzle—and sometimes, it's the piece that reveals the whole picture.
For iGaming operators, that can mean protecting promotional budgets, keeping games fair, reducing unnecessary friction, and most importantly, making sure that “new player” actually means new player.
FAQs for iGaming Operators
1. What is an AI fraud detection platform?
An AI fraud detection platform uses AI and machine learning to analyze large volumes of signals and identify patterns associated with fraudulent activity.
2. What are the benefits of using an AI fraud detection platform?
Detect fraud at scale
Identify complex patterns faster
Support more risk-aware decisions without relying only on fixed rules
3. What types of fraud can AI fraud detection platforms prevent?
They can help detect fraud such as:
- fake accounts
- multi-accounting
- bonus abuse
- account takeovers
- collusion
- payment fraud
- identity fraud.
4. How does device intelligence improve AI fraud detection?
Device intelligence adds visibility into the device and its environment, helping AI models connect accounts and identify signals such as emulators, app cloners, VPNs, and tampering.
5. What should businesses look for in an AI fraud detection platform?
Look for accurate device identification, real-time fraud signals, broad risk coverage, configurable controls, and the ability to work alongside existing fraud and identity systems.
6. What is the difference between AI fraud detection and traditional fraud detection?
Traditional systems often rely heavily on predefined rules, while AI-based approaches can analyze larger datasets and uncover evolving patterns and relationships.
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