Sign-up → Free play or small stakes → Deposit or withdrawal threshold → Document capture → Selfie and liveness → Face match → Account unlocked
The selfie is the weakest point in the chain. NeuroVerify adds an authenticity read at that point.
Gaming and fantasy sports / NeuroVerify
For real-money gaming, fantasy sports and betting operators that verify players before deposits and withdrawals. NeuroVerify checks the selfie or video for deepfakes and manipulation beyond liveness, so bonus abuse rings and borrowed identities are caught at the gate.
It fits the selfie step your verification already runs: one API call, no new step for the player, one unified risk score with signal-level evidence.
NeuroVerify does not estimate age, check documents or decide eligibility. It adds authenticity evidence for face, video and audio; your compliance and risk policy decide.

The gap
Gaming operators balance low friction against regulatory duties. Players sign up in seconds, play for free or small stakes, and are asked to verify at a deposit or withdrawal threshold. That verification is where identity, payments and responsible-gaming duties meet.
The incentives to cheat are direct. Sign-up bonuses, referral rewards and withdrawal of winnings are all tied to an account. Multi-account rings, borrowed identities and mule players have strong reasons to pass the selfie step with someone else's face or a generated one.
Document checks confirm a record. Liveness confirms a prompt was followed. Face match confirms similarity. None of them examines whether the face was generated, swapped, or injected through a virtual camera. A farm operating hundreds of accounts only needs a method that passes these checks once and can be repeated.
Sign-up → Free play or small stakes → Deposit or withdrawal threshold → Document capture → Selfie and liveness → Face match → Account unlocked
The selfie is the weakest point in the chain. NeuroVerify adds an authenticity read at that point.
Threats
These are attack techniques, not claims about any operator.
One operator creates many accounts, each with a different generated or swapped face, to collect bonuses at scale.
A person who cannot legally or safely play uses another person's identity, supported by a swapped face at selfie capture.
Accounts verified with synthetic faces receive and cash out funds on behalf of others.
Pre-recorded or generated video is fed to the capture step so the selfie never comes from a real camera.
A deepfake of the real owner passes the selfie re-check required for withdrawal or credential change.
A video stolen from a verified player is replayed across several accounts.
Rules for gaming and betting differ by jurisdiction. Your own compliance team defines what verification you need; NeuroVerify only supplies authenticity evidence for the face, video or audio step.
For wider marketplace and platform patterns, see deepfake detection for online marketplaces.
Where NeuroVerify fits
NeuroVerify sits at the selfie step before an account is unlocked for deposits or withdrawals. Your app sends the selfie or video it already captures. NeuroVerify runs six signals in one request and returns a unified risk score with signal-level evidence.
Your risk and compliance policy decides: allow, ask for another check, send to a reviewer, or block. Your document and payment checks stay as they are.
Pass the selfie or video from your app to NeuroVerify at deposit or withdrawal verification.
Get one score with evidence from the six signals.
Allow, step up, route to review or block using your thresholds.
Call the same API on withdrawal, payout method change or account recovery.
What you get
Stop fabricated faces where they unlock value.
Reduce mule players cashing out through synthetic identities.
Players take the same selfie. Nothing extra to do.
Signal-level detail supports faster, more consistent reviews.
Image checks return in under 200 ms p95 under defined test conditions.
Questions
No. NeuroVerify fits the selfie or video step your journey already runs. You send the media you already capture in one API call. There is no new step for the end user.
Integration uses native mobile SDKs (Android/Kotlin, iOS/Swift, Flutter), backend SDKs (Python, TypeScript/Node.js, Go, Java) or REST. Two lines of code is the product claim; live in under 24 hours is the target.
Not as a replacement. Liveness asks whether a live person was in front of the camera. NeuroVerify asks whether the media itself is authentic, and it includes a Live Presence Verification signal alongside five others, so it checks beyond liveness. See deepfake detection vs liveness detection.
You can keep your current liveness vendor and send the same selfie or video to NeuroVerify as well.
No. It does not estimate age and does not analyse identity documents. It checks face images, video and audio for deepfakes and manipulation. Age and document checks stay with your existing provider.
NeuroVerify scores each piece of media for authenticity. Detecting reuse across accounts is a separate analytic that your own systems may run. The authenticity score and signal evidence can feed those rules.
NeuroVerify returns in under 200 ms p95 on eligible image requests under defined test conditions. The test methodology is available on request, so you can compare it with your own traffic and network path.
Image checks run inside the capture step, so the end user does not see a separate wait. Video and audio take longer because they carry more media; ask us for numbers that match your clip lengths.
NeuroVerify runs as a managed cloud API, in a private cloud or your own VPC, as a secure container, on-premises, or air-gapped. The right option depends on your data-residency and security rules.
The same six signals and the same unified risk score are available in each model. Neural Defend holds ISO/IEC 27001, ISO/IEC 27701, ISO/IEC 42001 and SOC 2 Type II; see the Trust Center for details.
NeuroVerify returns one unified risk score plus signal-level evidence, so a reviewer can see which of the six signals raised concern. It does not make the final decision. Your own policy decides whether to approve, step up, route to manual review or decline.
Because thresholds are yours, you can tune them to your own tolerance for false positives and false negatives. See deepfake detection errors.
Yes. We recommend evaluating on your own media: your real capture conditions, devices and attack samples. Start with a small test set, compare the unified score and signal evidence against your current checks, then set thresholds with your own policy team.
NeuroVerify ranked #1 of 36 systems on VendorBench-100 (detection score MCC 0.876 versus 0.646 for the next-best vendor), but a public benchmark is a starting point. Your own media is the real test. Contact us to set one up.
Evaluate NeuroVerify
Send selfies from verified players and from confirmed abuse cases. See how the unified score separates them before you change your verification flow.