LOS and lending platforms / NeuroVerify
If you build or run a lending platform, your lender customers rely on your KYC step to keep synthetic and impersonated borrowers out. NeuroVerify gives that step a deepfake and manipulation check, returning one risk score with evidence per request.
It drops into the selfie or liveness step your platform already orchestrates, so lenders see no new step for borrowers, and you add one API call.
NeuroVerify does not verify documents, score credit or make lending decisions. It adds authenticity evidence; each lender configures its own policy.
The gap
Lending platforms stitch together vendors for identity, documents, bureau data, liveness and fraud. The platform returns one outcome to each lender, and lenders trust that the pipeline filtered out bad actors.
The gap is upstream of all those vendors. If the selfie or video is generated, swapped or injected, every vendor processes clean-looking input and returns a pass. The platform cannot tell its lender customers that the media itself was checked, because nothing in the stack checked it.
Borrower app or partner site → Identity and document vendors → Selfie or video session → Face match → Fraud and credit rules → Lender decision
The selfie or video is the only step carrying a live human. Manipulation there passes silently to every lender on the platform.
One weakness is shared across many lenders and many borrowers. Attackers that find a way through can reuse it at scale, and a single incident reaches every lender who depends on the platform. Embedding an authenticity check once, in the shared step, protects the whole book.
Face match and liveness remain useful, but they were not built to flag generated faces or injected streams. See Deepfake Detection vs Liveness Detection.
Threats
These are attack techniques, not claims about any specific platform or lender.
Automated scripts submit AI-generated faces to many lender journeys. Format checks and face match built from the identity photo pass them.
The attacker replaces the camera feed in a mobile or web SDK. The platform receives a well-formed stream that never came from a lens.
A live swap runs through a regulated video session offered by the platform. Prompts are answered by a real person behind another face.
The same fabricated persona applies to several lenders on the platform. Each lender sees one clean application.
A distributor or partner site submits manipulated selfies to hit volume targets, and the platform cannot tell it from genuine traffic.
A recorded selfie or clip from an earlier approved session is resubmitted for a new application. See replay attacks in identity proofing.
Background: digital injection attacks and synthetic identity fraud.
Platform buyers can also read Digital Consumer Lending Origination Review.
Where NeuroVerify fits
NeuroVerify sits behind the selfie or video capture in your platform, before face match and fraud rules. You call it once per borrower and expose the result to each lender in the shape their policy needs.
Borrower app or partner site → Identity and document vendors → Selfie or video capture → NeuroVerify → Face match → Risk score and per-lender rules
One call is added to the shared pipeline. Lenders keep their own thresholds and decisions.
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.
Your vendor stack, your multi-tenant configuration, each lender’s credit and fraud policy, your consent and retention rules, and the final decision for every borrower.
Pass the media your SDK or backend already receives from the borrower journey.
Use the REST API, a mobile SDK or a backend SDK inside your orchestration layer.
Store the score and signal evidence per application, and show it in your lender console.
Map score bands to approve, review or decline per lender, under their own rules.
What you get
You can show that borrower media was checked for deepfakes, not only for liveness.
Add the call once and offer it to every lender on the platform.
Score bands map to different actions for different risk appetites.
Two lines to integrate and live in under 24 hours, as a Neural Defend claim.
Run in managed cloud, a customer VPC, secure container, on-premises or air-gapped to meet lender requirements.
Questions
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 a platform, that avoids adding a second vendor next to the liveness provider you already offer. You can keep your current liveness vendor during an evaluation and compare outcomes on your own media. See deepfake detection vs liveness detection.
Yes. NeuroVerify returns a unified risk score and signal evidence. You map score bands to actions per lender, so each one applies its own policy.
After selfie or video capture and before face match and fraud rules, so every later vendor and rule can use the result.
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.
Mobile SDKs cover Android/Kotlin, iOS/Swift and Flutter, and backend SDKs cover Python, TypeScript/Node.js, Go and Java. An MCP server is available for AI agents.
NeuroVerify analyses the face images, video or audio you send and returns a risk score with evidence. Platforms often need tenant-level residency, so ask about private cloud and on-premises options early. 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.
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. Platform latency budgets are tight, so test end to end inside your own pipeline.
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.
Because you expose scores to lenders, you can offer different step-up paths per tenant.
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.
Run authorized samples from several lenders and channels through NeuroVerify next to your current checks. Compare scores and evidence against your current decisions. For a vendor shortlist, see the top 5 deepfake detection APIs.
Evaluate NeuroVerify
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.
