Why Banking Deepfake Solutions Don't Work for Insurance
Deepfake detection built for banking focuses on identity and voice — not the document, photo, and video evidence that drives insurance fraud.
The deepfake detection market has grown rapidly, driven primarily by demand from banking and financial services. Solutions like Reality Defender, Sensity, and others have built their products around the fraud vectors that banks face: voice cloning attacks on call centers, deepfake video calls impersonating executives, and synthetic identities bypassing KYC checks.
These are real threats, well-addressed by existing tools. But when an insurer evaluates these same tools for claims fraud detection, a fundamental mismatch emerges. Banking deepfake solutions detect fake people. Insurance needs to detect fake evidence.
Different Fraud, Different Detection
What Banks Need to Detect
Banking fraud through deepfakes follows a predictable pattern: someone pretends to be someone else.
Voice impersonation: A cloned voice calls the bank, impersonates a customer, and requests a transaction. The attack target is the person’s identity. The detection requirement is: is this voice real?
Video call impersonation: A deepfaked executive appears on a video call and authorises a transfer. The attack target is the person’s identity. The detection requirement is: is this face real?
KYC bypass: A synthetic or manipulated face is presented during identity verification. The attack target is the onboarding process. The detection requirement is: is this identity real?
In every case, the question is about identity authenticity — is this person who they claim to be?
What Insurers Need to Detect
Insurance fraud through deepfakes follows a fundamentally different pattern: someone presents fake evidence of a real or exaggerated event.
Manipulated damage photos: A genuine photo of minor vehicle damage is edited to show catastrophic damage. The claimant is real. The vehicle is real. The damage is exaggerated. The detection requirement is: has this image been manipulated?
AI-generated property damage: Photos of storm damage, fire damage, or water damage are generated or composited. The detection requirement is: is this damage real, or is the image synthetic?
Forged documents: A police report, medical record, or repair estimate is fabricated using AI text generation and document templating. The detection requirement is: is this document authentic?
Fabricated video evidence: Dashcam footage, security camera recordings, or property walkthrough videos are generated or manipulated. The detection requirement is: is this video genuine?
In every case, the question is about evidence authenticity — is this evidence a genuine record of what it claims to show?
The Gap
| Dimension | Banking Detection | Insurance Detection |
|---|---|---|
| Primary target | Identity (face, voice) | Evidence (photos, documents, video) |
| Attack type | Impersonation | Fabrication/manipulation |
| Key modalities | Face video, voice audio | Property photos, documents, dashcam video |
| Real-time requirement | Yes (live calls, video) | Less critical (claims submission) |
| Content type | Biometric (faces, voices) | Diverse (buildings, vehicles, injuries, text) |
| Detection method | Face liveness, voice authenticity | Pixel forensics, frequency analysis, metadata |
| Training data | Faces, voices | Property damage, vehicles, documents, injuries |
A tool optimized to detect whether a face on a video call is real will not reliably detect whether a photo of roof damage has been manipulated. These are different technical problems requiring different models, different training data, and different detection approaches.
Specific Technical Gaps
1. Training Data Mismatch
Banking deepfake detection models are trained primarily on:
- Face swaps (one face replaced with another)
- Face reenactments (expressions or movements manipulated)
- Voice synthesis (generated speech)
- Lip sync manipulation (video altered to match different audio)
Insurance fraud involves content these models have never seen:
- Property damage (roofs, walls, floors, landscaping)
- Vehicle damage (dents, scratches, structural damage)
- Medical imagery (injury photos, X-rays, medical reports)
- Environmental conditions (flooding, fire damage, storm debris)
- Documents (police reports, medical records, invoices)
A model trained on face detection cannot assess whether a photo of hail damage to a vehicle bonnet has been manipulated. The statistical patterns it looks for — face geometry, skin texture, eye reflections — don’t exist in property and vehicle imagery.
2. Compression and Quality Assumptions
Banking deepfake detection typically operates on:
- High-quality video streams (video calls, KYC video)
- Controlled capture conditions (webcams, professional cameras)
- Standardised formats (video conferencing codecs)
Insurance claims media is:
- Heavily compressed (photos taken on varied devices, shared via messaging apps)
- Variable quality (poor lighting, weather conditions, motion blur)
- Diverse formats (JPEG, HEIC, PNG, MP4, various document formats)
- Multi-generation (photos of photos, screenshots, re-saved files)
As we detail in our lab-to-production accuracy analysis, this quality gap causes detection tools optimized for clean media to dramatically underperform on real-world claims content.
3. Manipulation Types
Banking fraud typically involves whole-content generation — a fully synthetic face, a fully cloned voice. Detection looks for signatures of the generation process.
Insurance fraud often involves partial manipulation — a genuine photo with a specific region edited, a real document with altered figures, a legitimate video with a few frames modified. This is harder to detect because:
- Most of the content is genuine (passes integrity checks)
- The manipulation is localised (affects only a small region)
- The manipulation blends with surrounding genuine content
- Traditional detection looks for global artifacts, not localised ones
4. Metadata Context
Banking transactions have limited metadata context — a voice call is a voice call. Insurance claims have rich metadata that should be part of the verification:
- EXIF data (camera model, timestamp, GPS location)
- Weather conditions at claimed incident time and location
- Consistency between multiple photos in the same claim
- Consistency between photos and written description
- Document metadata (creation date, authoring software, edit history)
Banking detection tools don’t use this contextual metadata because it’s not relevant to their use case. For insurance, it’s a critical detection layer.
What Insurance-Specific Detection Requires
Multi-Layer Analysis
- Pixel-level forensics — Statistical analysis of pixel distributions, noise patterns, and compression artifacts to detect manipulation at the sub-visual level
- Frequency domain analysis — Spectral analysis to identify signatures of AI generation tools (GANs, diffusion models) that are invisible in the spatial domain
- Metadata verification — Cross-referencing EXIF data, timestamps, GPS coordinates, and camera model claims against known device databases and environmental records
- Semantic validation — Checking whether the content of the image is consistent with the claim description, weather records, and other evidence in the claim
- Cross-claim analysis — Identifying the same or similar images across different claims (recycled evidence detection)
Insurance-Specific Training
Detection models must be trained on data that represents what insurers actually receive:
- Property damage in Australian, US, UK, and European housing styles
- Vehicle damage across common makes and models
- Medical documentation in standard formats
- Claims photos taken on consumer devices in real-world conditions
- Documents from the jurisdictions where the insurer operates
Workflow Integration
Insurance detection must integrate into claims management platforms (Guidewire, Duck Creek, Majesco, custom systems) — not into video conferencing tools and call center platforms. The integration points, data flows, and user interfaces are fundamentally different.
The Evaluation Checklist
When evaluating deepfake detection for insurance, ask:
| Question | Why it matters |
|---|---|
| What types of content can you analyze? | Must include photos, documents, and video — not just faces and voices |
| What is your training data? | Must include property/vehicle/document content, not just biometric data |
| What accuracy do you achieve on compressed, mobile-captured photos? | Real-world insurance media, not lab-quality images |
| Can you detect partial manipulation (not just full generation)? | Insurance fraud often involves editing genuine photos, not generating from scratch |
| Do you analyze metadata as part of detection? | EXIF, timestamps, GPS — critical for insurance verification |
| Can you integrate with claims management platforms? | Guidewire, Duck Creek, not Zoom and Teams |
| Do you support cross-claim image analysis? | Detecting recycled evidence across claims |
| Are your forensic reports suitable for legal proceedings? | Evidence standards for claim denial and recovery |
If the vendor can’t answer these insurance-specific questions with confidence, their tool wasn’t built for your use case.
deetech is purpose-built for insurance. Our detection models are trained on insurance claims media, our analysis covers photos, documents, and video evidence, and our forensic reports meet the evidentiary standards required for claim denial and legal recovery. Request a demo to see the difference.