PAYMENTS & COMPLIANCE
Why gaming operators can’t rely on document checks to stop deepfakes
By Mira Sidhu, Director of Growth, Compliance Solutions (IDV) at SEON, the leading global fraud prevention solution that stops fraud before it happens.
S
he blinked. She smiled. She passed verifi cation. And she didn’t exist. A government-issued ID was uploaded.
A selfi e matched the document photo. Liveness checks confi rmed movement. The system approved her. But the identity was completely fabricated. For gaming operators dealing with bonus abuse, multi-accounting and underage access, this is happening more often. Synthetic identity fraud caused over $35 billion in losses in 2023, and deepfake technology keeps getting easier to use.
The problem isn’t the verifi cation steps, it’s what they don’t catch. Most systems confi rm what’s visible but miss the signals that matter. Deepfakes can fake a face but not a digital history. They can match a photo but not years of online behaviour.
WHY DOCUMENT CHECKS MISS THE MARK ID uploads, selfi e captures and liveness detection were built for simpler threats. They assume that if something looks real, it is real. That’s no longer true.
Fraudsters use tools like DeepFaceLive and Amigo AI to alter their appearance in real time during verifi cation. With a stolen ID, they match their live selfi e to the document photo. The biometric engine sees a match and lets them through.
These attacks work because most systems only check what’s in front of the camera. They verify the image but never question who’s behind it.
WHAT GENUINE USERS ACTUALLY LOOK LIKE
Real people leave digital trails: social profi les, consistent patterns across services, data showing up in breach records. Fraudsters don’t have that. They show up with brand-new email addresses, burner devices and profi les created days before signup. The face might pass, but the data underneath tells a different story.
Context matters more than appearances. Real identity isn’t about looking the part, it’s about having a past.
HOW RISK-BASED VERIFICATION WORKS
Regulators like FinCEN want dynamic approaches, not checkbox compliance. They expect platforms to understand who’s behind the data, not just whether it matches. Risk-based verifi cation starts with signals users give off at signup: email age, phone history, IP
address, device intelligence, behavioral patterns. These expose synthetic identities before document checks even happen. Verifi cation becomes smarter from there. A perfect face match still triggers fl ags when the email was registered yesterday or there’s no digital footprint. Risk scores decide what happens next: low-risk users move forward; suspicious sessions get reviewed or blocked.
For gaming operators, this cuts straight to the problem. Bonus abusers and multi-accounters get stopped before they waste your resources.
WHY THIS APPROACH CHANGES EVERYTHING
Fewer fake accounts make it through. Review queues get shorter. KYC costs drop. Analysts spend time on real edge cases instead of obvious fraud. The system learns and improves with every session. More importantly, it protects your legitimate players. When fraudsters can’t get in, real users notice.
VERIFICATION HAS TO START SOMEWHERE ELSE
The question isn’t whether a face looks convincing anymore. It’s whether the person actually exists. Static checks capture one moment. Real identity builds over time. The best systems know this. They don’t stop at appearances; they ask harder questions. Does this user act like a real person? Is there proof they’ve existed online before today?
Deepfakes can fake a lot, but they can’t fake a digital past. If there’s no history, there’s no trust. That’s where verifi cation needs to start, not with the image on screen, but with everything underneath it.
GIO MARCH 2026 13
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80
