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Neural Defend

Industries NeuroVerify · NeuroDetect · NeuroCall

Deepfakes don’t target industries. They target the moment a face, a voice or a file counts as proof.

Banks open accounts on a video selfie, help desks reset access on a familiar voice, and investigators rule on a submitted photo. Neural Defend checks that face, voice or file where your people or policy decide: NeuroVerify at the checkpoint, NeuroDetect in the investigation, NeuroCall (Beta) on the call.

The problem, in numbers

Identity providers, regulators and the FBI are describing the same shift.

Six industries

Same fakes. Different doors.

Each card names the workflow most exposed, how the attack works there, and which product covers it. Pick what gets faked to see where it enters each industry.

What gets faked

A face reaches 6 of 6 industries. Liveness asks whether someone is in front of the camera. It does not ask whether the face is real, or whether a camera was used at all.

  • BFSI

    Remote account opening and video KYC

    An injected or face-swapped stream passes the liveness prompt, and the account opens for a money mule.

    • Selfie and video KYC
    • Contact-centre payee and payment changes
    • Altered statements and evidence in fraud cases
    Read the BFSI page
  • Fintech

    Account recovery and device re-binding

    A generated selfie moves the account to the attacker’s phone. Every login after it looks legitimate.

    • Onboarding and recovery selfies
    • Support calls asking for a reset or a higher limit
    Read the Fintech page
  • Government

    Official communications and case evidence

    An AI voice message from a “senior official” moves the target to another app. Fabricated media enters a case file.

    • Remote identity proofing for public services
    • Calls and voice notes from “officials”
    • Submitted and seized media in investigations
    Read the Government page
  • OEMs and platforms

    Identity and media checks inside your product

    Your customers meet injected streams and cloned voices. The check has to run inside your app, device or pipeline.

    • Face capture in your app, SDK or device
    • Voice channels your platform carries
    • Media your platform ingests
    Read the OEMs and platforms page
  • Enterprise

    Payment approvals and help-desk resets

    A “CFO” on a video call approves the transfer. A cloned voice asks the help desk for a new MFA device.

    • Remote hiring and identity re-checks before a reset
    • Help-desk and executive calls
    • Recordings kept for incident review
    Read the Enterprise page
  • Marketplaces and consumer apps

    Seller onboarding, refunds and account recovery

    A generated face admits a fake seller. An AI-edited photo claims a refund for a parcel that arrived intact.

    • Seller verification and recovery selfies
    • Refund and dispute photos
    Read the Marketplaces and consumer apps page

Product to workflow

Faces at the checkpoint. Files with the investigator. Voices on the call.

The split follows the evidence, not the sector. A bank and a marketplace use the same product for a selfie and a different one for a disputed photo.

ProductWhat it examinesWhere it sitsWorkflows it coversWhat you get

A face image, video or audio sample, in one API call.

At the checkpoint: onboarding, recovery, re-verification before a reset.

BFSI video KYC · fintech recovery · seller verification · OEM SDKs · enterprise help-desk resets

One risk score with six signals behind it. Your policy decides. It does not analyse ID documents.

Images, audio, video and documents, uploaded into a case workspace.

After a flag, with an investigator or analyst.

Government investigations · bank fraud cases · refund disputes · enterprise incident review

Heatmaps and region, frame and segment findings, a plain-language summary and a forensic report.

The caller’s voice, during the call.

In the contact centre, help desk or official line.

Bank contact centres · fintech support · enterprise help desks · government lines

Call and voice deepfake detection in Beta. Fit for your telephony is confirmed in evaluation.

Sources

Read the evidence yourself.

Third-party publications. None of them evaluates or endorses Neural Defend.

Questions

What buyers ask before they pick a product.

Compare the top 5 deepfake detection APIs
Which product do we start with?

Follow the evidence. If a face or voice decides whether someone gets in, start with NeuroVerify at that checkpoint. If an investigator rules on a photo, recording or document, start with NeuroDetect. If the decision happens on a live call, talk to us about NeuroCall, which is in Beta.

We already run liveness. Is that not enough?

Liveness checks that a person is present. A face swap that blinks on cue, or a stream injected past the camera, can still pass. NeuroVerify checks whether the face itself was manipulated or generated. Detection vs liveness

Does NeuroVerify check ID documents?

No. NeuroVerify analyses face images, video and audio. Documents under investigation go to NeuroDetect, where an analyst reviews them.

Can it run inside our own environment?

NeuroDetect runs as a web app, in your private cloud or VPC, as a secure container, on-premises or air-gapped. Neural Defend holds ISO/IEC 27001, 27701 and 42001 certifications and a SOC 2 Type II report. Deployment options

Will a detection result make us compliant?

No product does that. Neural Defend supplies evidence: a score with its signals, or a forensic report. Your policy, your analysts and your counsel decide what it means for a regulator.

Our industry is not listed. Does that matter?

Rarely. The risk sits in the workflow, not the sector: account opening, recovery, payment approval, evidence review. Pick the page whose workflows look most like yours, or bring yours to an evaluation.

See it on your own media

Bring the workflow where you trust a face, a voice or a file.

Bring sample selfies, call recordings or case files from that workflow, including ones you know were fake. See what NeuroVerify and NeuroDetect find, and what the evidence looks like on file.