Operators should combine friction at registration with strong device intelligence so they can recognize repeat sign-up behavior without relying only on cookies or IP addresses. The goal is to identify the same device, browser, or network patterns across many accounts, then score risk before bonuses are issued. This approach protects acquisition budgets while preserving a smoother path for genuine players.
Why device intelligence works better than cookie-only screening
new account fraud in iGaming is usually a scale problem, not a single-bad-actor problem. Fraudsters can refresh cookies, rotate emails, and swap accounts quickly, so the practical question is whether the operator can recognise repeat behaviour across a device, browser, or network pattern before incentives are released. That is why strong device intelligence is more useful than relying on one weak signal.
Operators should think in terms of pattern continuity. A legitimate player may create one account and move on; a fraud ring tends to reuse infrastructure, timing, and behavioural traits across many registrations. The most effective controls separate identity proofing from risk scoring, so the sign-up flow can stay light while the fraud decision becomes more selective where the evidence is stronger.
That approach is strengthened by broader account-governance discipline, especially where repeated registrations may be tied to shared infrastructure or reused credentials. Ultimate Guide to NHIs is useful here because it explains how lifecycle visibility and access governance reduce blind spots around repeated access patterns, while Top 10 NHI Issues is a good navigation point for the related visibility, sprawl, and excessive-permission problems that tend to amplify abuse at scale.
How to add friction without turning away good players
The balancing act is to add friction only where it changes the fraud decision. Registration should be fast for low-risk users, but the operator should be ready to increase challenge when the same device cluster, subnet, browser fingerprint, or behavioural sequence keeps appearing across fresh accounts. That lets you preserve conversion for genuine players while making mass account creation expensive and less reliable.
In practice, the best signals are layered rather than absolute. Device intelligence is strongest when combined with velocity checks, bonus abuse indicators, and historical linkage across prior sign-ups. None of those signals should be treated as proof on their own. A higher-risk score should trigger proportionate step-up review or bonus delay, not an automatic block unless the pattern is well above the operator’s tolerance.
For practitioners, the useful discipline is to measure false positives by journey stage. A sign-up control that is too aggressive may protect the bonus budget but quietly damage acquisition, affiliate conversion, and returning-player trust. A control that is too soft will leave the operator funding repeat abuse. The right design makes the fraud decision later than the registration decision whenever possible.
Risk and Threat Considerations
New account fraud is attractive because the attacker’s cost is low and the operator’s loss can scale quickly through bonus abuse, payment abuse, chargeback exposure, and repeated promotional spend. The main failure mode is overreliance on a single identifier such as a cookie or IP address, which is easy to reset or share across many attempts.
Failure mechanism: Fraud rings create many accounts from the same underlying device or infrastructure while rotating superficial identifiers, then wait until the bonus or first-deposit trigger before cashing out or laundering value through legitimate-looking play.
Impact: Operators absorb acquisition waste, distorted player-quality metrics, and higher manual-review load, while genuine users can face unnecessary friction if the control does not distinguish repeat abuse from normal sign-up variation.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 address the attack and risk surface, while CIS Controls v8 and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-03 — Visibility and Discovery | Device-linked repeat sign-up patterns rely on discovery and linkage visibility. |
| NHI-05 — Overprivileged Access | Bonus abuse becomes easier when accounts or linked systems have excessive access or reach. | |
| NHI-08 — Secrets and Credential Lifecycle | Fraud rings often reuse or rotate identifiers, so lifecycle discipline matters to abuse detection. | |
| Recommendation — Use discovery and visibility controls to link repeated registration patterns before bonuses are issued. Restrict access and bonus pathways so linked accounts cannot escalate value quickly. Rotate and retire reusable access material quickly to reduce repeat-abuse opportunities. | ||
| CIS Controls v8 | CIS 5 — Account Management | New account fraud is directly constrained by account creation and account governance controls. |
| CIS 6 — Access Control Management | Risk-based step-up access and bonus gating are access-control decisions. | |
| CIS 8 — Audit Log Management | Repeat-registration detection depends on auditability across sign-up events and linked sessions. | |
| Recommendation — Enforce strong account management checks before allowing incentives or high-risk access. Apply risk-based access control to delay bonuses when linkage signals exceed tolerance. Retain sign-up and linkage logs so fraud analysts can confirm repeat-behaviour patterns. | ||
| NIST CSF 2.0 | ID.AM — Asset Management | Device intelligence depends on knowing and correlating the assets involved in repeated sign-up behaviour. |
| PR.AA — Identity Management, Authentication and Access Control | Balancing friction and legitimate sign-ups depends on proportionate authentication and access decisions. | |
| DE.CM — Security Continuous Monitoring | Fraud scoring requires ongoing monitoring for recurring patterns across registrations. | |
| Recommendation — Inventory and correlate devices and sessions to identify repeated abuse patterns. Use proportionate step-up checks when risk rises instead of blocking all users. Continuously monitor sign-up behaviour for repeated infrastructure and velocity signals. | ||
Practitioner Guidance
What to prioritise: Build a risk score that is available before bonus issuance, not only after account creation. The highest-value controls are the ones that let you defer rewards or step up verification when device-linkage confidence is high enough to matter.
What to verify: Check that the fraud model uses more than one signal family, including device intelligence, browser consistency, network reputation, and registration velocity. If all of those signals point to the same cluster, treat it as a linkage problem rather than a single suspicious session.
Decision rule: If the pattern suggests repeated infrastructure reuse across multiple new accounts, delay incentives first and review the case second. If the signals are weak or isolated, preserve the signup path and let later behavioural monitoring absorb the risk.
Practitioner takeaway: The objective is not to stop every risky sign-up at the door, it is to identify repeat abuse early enough that you can protect bonuses and preserve a low-friction journey for legitimate players.
Related resources from NHI Mgmt Group
- How should banks and fintechs reduce new account fraud without making sign-up too slow for legitimate customers?
- How should financial institutions reduce account takeover risk without blocking legitimate customers?
- How should security teams reduce identity fraud without blocking legitimate users?
- How should gig platforms reduce identity fraud without blocking legitimate users?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on September 18, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
