Application → Bureau and identity record checks → Selfie or video session → Face match → Credit decision → Card issued and activated
The selfie or video is the only live-human evidence. Manipulation there leads straight to a usable credit line.
Card issuing / NeuroVerify
Deepfake Detection API for Online Credit Card Applications
Card issuers approve and ship cards in minutes from a selfie or video session. NeuroVerify checks that media for deepfakes, face swaps and injected video, and returns one risk score with evidence before the card is issued.
It fits the selfie or liveness step your card journey already runs: no new step for the applicant, one API call.
NeuroVerify does not run bureau checks, verify documents or approve credit. It adds authenticity evidence; your credit and fraud policy decides.
Powered byNeuroVerifyExplore the product
- #1 of 36on the public VendorBench-100
- <200 msp95 on eligible image requests
- 6 → 1six checks, one unified score
- 0new steps for your customer
The gap
The bureau can pass and the face can still be synthetic.
Online card applications combine a credit bureau pull, identity record checks, device and velocity signals and a selfie matched to the identity photo. Approval is often instant, and the card can be used soon after.
Those checks tell the issuer that data is consistent and that credit is available. They do not tell the issuer whether the face on camera is a real person who is present. A fabricated applicant built on real data, or a stolen identity paired with a swapped face, can pass them all.
Why card fraud moves fast
Once a card is active, losses build quickly and the true identity owner may not know until later. Stolen identities and synthetic personas are common routes to this kind of abuse, and generated faces remove the need for a stolen photo.
Face match and liveness stay useful, but neither was designed to flag a generated face or an injected stream. See Deepfake Detection for Fintech.
Threats
How attackers get a card in someone else’s name.
These are attack techniques, not claims about any specific issuer.
01
Synthetic identity built for credit abuse
Real identifiers are mixed with a generated face to build a persona that builds credit over time and then disappears.
02
Stolen identity with a swapped face
An attacker holds a victim’s identity data and uses a face swap at the selfie step so the face matches the photo on record.
03
Generated selfie matched to the reference photo
An AI-generated selfie is built from the reference photo and passes face match because both images share a source.
04
Real-time face swap in video verification
A live swap runs during a video session. Prompted actions are performed by a real person behind another face.
05
Virtual camera and stream injection
The camera feed in the mobile app is replaced, so the issuer receives a clean stream that never came from a lens.
06
The same synthetic face across issuers
One manipulated face is used on applications to many issuers at once, and each issuer sees a single clean file.
Background: digital injection attacks, synthetic identity fraud and FinCEN deepfake fraud alert.
Where NeuroVerify fits
An authenticity check before the credit line opens.
NeuroVerify sits between media capture and face match in your card journey. It checks the selfie or video you already collect and leaves your bureau, identity and credit logic as they are.
Application → Bureau and identity checks → Selfie or video capture → NeuroVerify → Face match → Risk score and credit policy
One call is added. Your vendors and credit engine keep their roles.
What runs in one request
NeuroVerify checks the face image, video or audio for deepfakes and manipulation beyond liveness. All six signals run together and resolve into one risk score:
- Visual Safety Screening - Live Presence Verification - Face Manipulation Detection - AI Generative Model Analysis - AI Pixel-Level Manipulation - AI Content Compliance
The response carries the unified score plus signal-level evidence. Your own policy makes the final decision.
What stays with you
Your bureau and identity vendors, your credit and fraud rules, your card activation controls, your consent and retention rules, and the decision on every application.
NeuroVerify Unified Trust LayerSix parallel analysis layers
- Visual Safety Screening
- Live Presence Verification
- Face Manipulation Detection
- AI Generative Model Analysis
- AI Pixel-Level Manipulation
- AI Content Compliance
Send the applicant selfie or video
Pass the image or frames your app or web journey already captures.
Call one API
Use the REST API or a mobile or backend SDK in your origination flow.
Read the unified risk score
Store the score and signal evidence on the application.
Route by your policy
Approve, hold activation for review or decline under your own thresholds.
What you get
What card issuers get.
Fewer fraudulent accounts opened
Swapped and generated faces are flagged before a credit line exists.
Instant approvals stay instant
The check runs inside the existing selfie step, so genuine applicants see no extra friction.
Better evidence for fraud teams
Signal-level output helps investigators understand why a file was held.
A safeguard before activation
High-risk results can hold card activation or shipping for review.
One check for app and web
The same API covers mobile app and web journeys.
Questions
What teams ask before they integrate.
We already run liveness. Do we still need NeuroVerify?
Liveness answers whether a live person appears to be in front of the camera. It was built for presentation attacks such as a printed photo, a mask or a screen held to the lens. It was not built to tell whether the stream reaching your server came from a camera at all, or whether the face inside it was generated or swapped.
NeuroVerify runs inside the selfie or video step you already have. Live Presence Verification is one of its six signals, and the other five look for face manipulation, generative-model traces, pixel-level edits and unsafe content in the same request. For cards, that means the selfie step also checks for swapped and generated faces. You can keep your current liveness vendor during an evaluation and compare outcomes on your own media. See deepfake detection vs liveness detection.
Does it replace our bureau or identity vendor?
No. NeuroVerify adds authenticity evidence next to those vendors. Bureau data, identity records and credit decisions stay where they are.
Where does it sit in the application flow?
After selfie or video capture and before face match and the credit decision, so downstream rules can use the score.
How hard is it to integrate?
NeuroVerify offers native mobile SDKs (Android/Kotlin, iOS/Swift, Flutter), backend SDKs (Python, TypeScript/Node.js, Go, Java) and a REST API. Integration is two lines of code, and teams can be live in under 24 hours.
The call fits mobile app, web and partner-led card journeys. See integrating into an existing KYC stack.
How is our data handled, and where can it run?
NeuroVerify analyses the face images, video or audio you send and returns a risk score with evidence. NeuroVerify does not process bureau data or identity documents. Consent, retention and residency stay under your policy. See product data handling for how media is treated.
Deployment options include the managed cloud API, private cloud or customer VPC, a secure container, on-premises and air-gapped environments. Neural Defend holds ISO/IEC 27001, ISO/IEC 27701, ISO/IEC 42001 and SOC 2 Type II certifications, shown on the trust page.
How fast is it, and will it slow the journey?
NeuroVerify targets under 200 ms at p95 on eligible image requests under defined test conditions. The test methodology is available on request, so you can check it against your own media sizes and network path.
It is one API call on media you already capture, so it adds a single request rather than a new user step. Instant card approvals have tight budgets, so test end to end during evaluation.
How do we handle false positives and set thresholds?
NeuroVerify returns one unified risk score and signal-level evidence. It does not approve or reject anyone. Your policy sets the thresholds and decides what each band does.
Many issuers hold activation for mid-band results instead of declining, which protects genuine applicants.
Can we test it on our own media first?
Yes, and that is the test that matters. Public results help: NeuroVerify ranked first of 36 systems on VendorBench-100, with a detection score of MCC 0.876 against 0.646 for the next-best vendor. But your capture devices, compression and attack mix decide real performance.
Use authorized samples from app, web and video paths, including thin-file and edge-case applicants. Compare scores and evidence against your current decisions. For a vendor shortlist, see the top 5 deepfake detection APIs.
Evaluate NeuroVerify
Test NeuroVerify on your card application media.
Bring authorized media from your own journey. NeuroVerify returns a risk score with signal-level evidence, so you can compare it with your current checks before changing anything.