What is bonus abuse fraud?

Bonus abuse fraud is the exploitation of promotional offers by a bonus abuser who uses multiple identities to claim the same offer repeatedly. Casino bonus abuse is the most common form, and operators report it accounts for around 60–64% of total iGaming fraud activity. Welcome bonuses and signup credits claimed once per real person, then again under fake accounts.; Free bets and deposit matches farmed at scale across coordinated account farms.; Refer-a-friend payouts triggered between accounts the same operator controls. ShieldLabs lets your team identify bonus abuse through persistent visitor and user identification across sessions, regardless of email, IP, or cleared cookies.

What is bonus hunting and how is it different from bonus abuse?

Bonus hunting (or a bonus hunt) is the practice of opening accounts specifically to claim promotional offers like welcome bonuses, free spins, and deposit matches, without intent to become a regular customer. The line between hunting and abuse is the number of identities involved. Bonus hunting: one bonus hunter using a single identity to chase offers; this may violate the spirit of a promotion but not its rules.; Casino bonus hunting / bonus arbitrage: a player mathematically exploits wagering requirements for guaranteed profit, still under one identity.; Bonus abuse: the same person uses multiple identities or accounts to claim one offer repeatedly, which crosses the line into policy and fraud territory. ShieldLabs identifies the abuse case by linking accounts back to the same visitor and user across sessions, even when each account uses a different email, IP, or browser.

What is account farming?

Account farming is the practice of creating and operating multiple user accounts on a single platform, usually under fake or synthetic identities, to claim bonuses, referral payouts, or promotional offers at scale. The goal is to disguise one operator behind many identities. Anti-detect browsers that fake a fresh device fingerprint for each account.; VPN and proxy tunnels that swap the egress IP per session.; Prepaid cards and disposable emails that make each account look unrelated. ShieldLabs links farmed accounts back to the same visitor and user through device fingerprinting, anonymity signals, and pre-built Patterns that correlate many accounts on one device.

How do online casinos and operators detect multiple accounts?

Online casinos and operators detect multiple accounts by combining identity and anonymity signals into one persistent view of each player, then correlating the accounts that resolve to the same person. No single signal is enough, so they layer several together: Device fingerprinting that holds across sessions, cleared cookies, and incognito.; Anonymizer detection for VPN, proxy, Tor, and anti-detect browser traffic, plus IP reputation.; Pre-built patterns like many accounts on one device, which surface the multi-accounting behind most bonus abuse before payouts go out. Layered together, these signals link farmed accounts back to one player even when each account uses a different email, IP, or browser.

Is bonus abuse illegal?

Bonus abuse usually breaches an operator's terms of service rather than criminal law. Criminal prosecution is rare unless stolen identities or stolen payment methods are involved. Terms-of-service breach (most cases): void winnings, close accounts, and refuse withdrawals under the promotion's own rules.; Criminal territory: stolen identities or payment methods can escalate a case beyond an operator's terms.; Jurisdiction-specific: in some gambling markets, the local regulator may also weigh in. ShieldLabs gives operators the data layer to enforce their own terms, with persistent visitor and user identification, anonymity signals, and Patterns, so the policy decision stays with the operator.

How does ShieldLabs prevent bonus abuse?

ShieldLabs returns persistent visitor and user identification with a 0–100 Risk Score on every signup, deposit, and bonus claim, so your team can build prevention rules on top of it. Bonus abuse detection runs upstream of those rules, before any payout goes out. Persistent identification: the same person is recognized across cleared cookies, incognito mode, and rotated IPs.; Patterns: when one person opens a second account for a welcome bonus, the multi-accounting Pattern fires with a severity level (Suspicious or Dangerous).; 100+ signals: device, OS, browser, IP, and network signals (including anti-detect browser detection, VPN, and proxy) feed the score. ShieldLabs hands operators across iGaming, fintech, crypto, and SaaS the signals to set their own thresholds for verification, payout hold, or block.

Will ShieldLabs automatically block bonus claims?

No. ShieldLabs blocks nothing automatically; it returns a Risk Score and full Details breakdown, and your code decides what to do with each bonus claim. Lower scores: credit the bonus normally with no added friction for real customers.; Mid-range scores: require additional verification before crediting the bonus.; High scores: hold the payout for manual review. ShieldLabs includes 5,000 free identifications so you can tune these thresholds on real traffic before enabling any rules in production.

Bonus Abuse Prevention

Stop bonus abuse draining promo budgets

Keep welcome bonuses, deposit matches, and referral payouts on real customers, and marketing spend on real growth

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Stop account farms

Surface coordinated rings running multiple identities to drain promotional budgets at scale

Block repeat bonus claims

Spot the same person across emails, wallets, and devices claiming the same offer twice

Protect promo budget

Keep welcome bonuses, signup credits, and deposit matches going to real customers

Keep real users friction-free

Legitimate users claim bonuses and complete signup without extra verification or holds

How ShieldLabs prevents bonus abuse

ShieldLabs recognizes the real visitor behind every anonymous session, so your team can stop fraudulent bonus claims in real time

Accurate Identification

Identify returning visitors and users across sessions, cleared cookies, incognito mode, and rotated IP

Anonymity Signals

Detection of VPN, proxy, Tor, anti-detect browser, IP reputation and other anonymity signals on every visit, with up to 99% accuracy

Risk Score

A ready-to-use score reflecting the risk of each visit, with the weight of every signal behind it

Ready-Made Patterns

Detection of the hidden connections between accounts that may signal multi-accounting, account farms, and more

Real-Time Analytics

See how much of your traffic is masked, with an overall quality score and a breakdown of the sources sending your visits

Start preventing bonus abuse in 5 minutes

Easily integrate into any signup, deposit, or bonus redemption flow

1
Create your account
Get 5,000 free identifications
2
Add the snippet
It identifies every visitor and returns their anonymity signals and a risk score
3
Check your traffic quality
See how much of your traffic is masked, with an overall traffic score
4
Use the API and Webhooks
Apply the risk score and anonymity signals in your rules to stop fraud and abuse

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api.shieldlabs.ai/v1/visits/latest

{
  "request_id": "0c284695-cf0b-4755-8beb-0a2e9536595e",
  "visitor_id": "aa8c616a-8a25-4a5e-bee2-a9a08e5128a4",
  "device_id": "6a45967d-1371-9652-ba99-b01ea3992208",
  "user_hid": "u_9f2a41c7",
  "public_ip": { "ip": "62.197.149.124", "country": "United States" },
  "local_ip": { "ip": "45.83.91.7", "country": "United States" },
  "connection_type": "vpn",
  "os": "Windows",
  "browser": "Chrome",
  "device_type": "desktop",
  "risk_score": 85,
  "signals": [
    { "name": "antidetect_browser", "weight": 60 },
    { "name": "vpn", "weight": 15 },
    { "name": "timezone_mismatch", "weight": 10 }
  ]
}

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