Fintech and payments / NeuroVerify
For wallets, neobanks, BNPL, remittance and payment providers that onboard users in minutes. NeuroVerify checks the onboarding selfie or video for deepfakes and manipulation beyond liveness, so mule and synthetic accounts are caught before they move money.
It fits the selfie step your onboarding already runs: one API call, no extra step for the customer, one unified risk score with signal-level evidence.
NeuroVerify does not replace your KYC, sanctions, transaction monitoring or credit decisions. It adds authenticity evidence for face, video and audio; your risk policy decides.
The gap
Fintech growth depends on short onboarding: a few taps, a selfie and an account. That speed is the product, and it is also the attack surface. Every extra manual check costs conversion, so the selfie and liveness step carries a lot of weight.
The accounts that matter most to fraudsters are cheap to open and fast to monetise: mule accounts for moving stolen funds, wallets for cash-out, and synthetic identities built to receive credit. A convincing generated or swapped face is now a low-cost tool for opening them at scale.
Liveness confirms a prompt was followed. Face match compares the selfie with a document photo. Neither tells you whether the face was generated, swapped or injected through a virtual camera. A synthetic face can match a synthetic document photo and pass both. See deepfake detection vs liveness detection.
App download → Phone and email verified → Document or registry check → Selfie and liveness → Account and wallet opened → First funding or transfer
Fraud often shows up only after the first transfer. NeuroVerify moves the authenticity check to the selfie step.
Threats
These are attack techniques, not statements about any specific company.
Fraud rings open many accounts with generated or swapped faces to receive and forward stolen funds. See mule account enrolment with synthetic media.
A fabricated identity with an AI-generated face is aged, then used to get BNPL or credit lines that are never repaid.
Scripts and virtual cameras feed prepared video into the app, so the selfie never comes from a real lens.
A deepfake of the real customer defeats a selfie re-check used for device change, limit increase or recovery.
A fake owner or beneficial owner appears on video during merchant onboarding to open a collection account.
An attacker passes verification as an existing sender, then redirects transfers to accounts they control.
Authorities have flagged deepfakes in financial fraud; see the FinCEN deepfake fraud alert. The pattern applies in any market, from card programmes to instant-payment networks.
For design guidance, see digital wallet enrollment and recovery and merchant onboarding control review.
Where NeuroVerify fits
NeuroVerify sits at the selfie and video step, before an account or wallet is opened. Your app sends the media it already collects. NeuroVerify runs six signals in one request and returns a unified risk score with signal-level evidence.
Your risk engine combines that score with device, behaviour and document signals. You choose what happens next. Nothing in your KYC stack is replaced.
Post the selfie or video from your app to NeuroVerify with the native SDK or REST.
Get one score and evidence from each of the six signals.
Combine the score with device and behaviour signals. Approve, step up, route to review or decline.
Call the same API for device change, limit increases and account recovery.
What you get
Stop fabricated faces at account opening, before funds move.
Customers take the same selfie. The check runs behind it with no added step.
Use the same API at onboarding, recovery and step-up for sensitive actions.
Signal-level evidence lets analysts triage flagged cases quickly.
Native mobile SDKs and backend SDKs. Live in under 24 hours is the target.
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. Native Android, iOS and Flutter SDKs keep the capture experience as it is.
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. Liveness vendors typically focus on presentation attacks. NeuroVerify adds checks for generated and manipulated media.
Yes. Many fintech teams use the same API at account recovery, device change, limit increase and high-value payment authorisation. See deepfake detection for fintech.
No. NeuroVerify checks face images, video and audio. Document verification stays with your current provider; NeuroVerify protects the selfie and video evidence next to it.
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 and video from real onboarding and from known fraud cases. See how the unified score separates them before you change a single flow.
