Insurance Claim Video Verification: The AI Approach

Video evidence is becoming central to insurance claims. Dashcam footage for auto accidents. Smartphone walkthrough videos of property damage. Surveillance recordings for liability disputes. Recorded statements from claimants and witnesses.

As insurers increasingly accept and rely on video evidence, the ability to verify that video hasn’t been manipulated becomes a critical capability — one that most insurers currently lack.

Why Video Verification Matters Now

The shift toward video-based claims evidence has accelerated for several reasons:

Consumer technology. Virtually every policyholder carries a high-definition video camera in their pocket. Dashcams are standard in many vehicles. Home security cameras are ubiquitous. The volume of video evidence available for claims has grown enormously.

Insurer encouragement. Many insurers actively request video evidence. Mobile claims apps prompt policyholders to record video walkthroughs of damage. Some auto programs incentivise dashcam installation. Video provides richer context than photos alone, helping adjusters assess claims more accurately.

Remote assessment. The acceleration of digital-first claims handling — a trend that began before COVID-19 but was dramatically accelerated by it — means more claims are assessed entirely from submitted media without physical inspection. Video fills the gap that would otherwise require an in-person visit.

The deepfake problem. Generative AI can now manipulate video with increasing sophistication. While video deepfakes are currently harder to produce convincingly than image deepfakes, the technology is advancing rapidly. According to Sumsub’s 2024 Identity Fraud Report, identity fraud rates have surged from 1.10% in 2021 to 2.50% in 2024, with deepfakes identified as the primary AI-driven attack vector — and video manipulation is a growing component of that trend.

How Video Can Be Manipulated

Understanding the attack vectors helps frame the detection challenge.

Frame-Level Manipulation

Individual frames within a video can be altered — damage added, timestamps changed, objects inserted or removed. If done frame-by-frame with consistency, the resulting video can appear seamless to casual viewing. This is the video equivalent of photo manipulation, applied across hundreds or thousands of frames.

Temporal Splicing

Sections of video from different recordings can be spliced together to create a false narrative. A genuine video of a damaged vehicle might be spliced with unrelated footage to fabricate a sequence of events — for example, inserting staged footage of another vehicle “causing” the damage before the genuine damage shots.

Face and Voice Synthesis

For recorded statements, depositions, or video calls, deepfake technology can replace faces, alter lip movements to match different speech, or generate entirely synthetic faces and voices. The Hong Kong deepfake CFO case reported by CNN in February 2024 — where a finance worker was tricked into transferring US$25.6 million via a video call with entirely deepfaked participants — demonstrated that live video synthesis is already convincing enough to fool professionals.

Metadata Manipulation

Video file metadata (creation date, device information, GPS coordinates, duration) can be stripped or altered to misrepresent when, where, and how a video was recorded. A video recorded months earlier can be given a timestamp matching a recent claim. Footage from a different location can have its GPS data changed.

Video Generation

The most advanced threat is end-to-end video generation from text prompts or reference images. While current AI video generation tools (Sora, Runway, Pika) produce output that is often detectable, the quality is improving with each model generation. Within the near future, generating a plausible 30-second walkthrough of fabricated property damage may be feasible.

The Human Limitation

Claims adjusters can catch obvious video manipulation — visible jump cuts, clearly mismatched audio, obvious visual glitches. But sophisticated manipulation operates below the threshold of human perception.

Consider the scale of the challenge:

A single 30-second video at 30 frames per second contains 900 individual frames
Subtle inconsistencies between frames — slight color shifts, minor geometric distortions, imperceptible flickering — are invisible to human viewers watching at normal speed
Metadata analysis requires technical tools, not visual inspection
Audio deepfakes targeting voice characteristics operate in frequency ranges that human ears cannot reliably distinguish

Manual video review is necessary but insufficient. The volume of video evidence, the subtlety of sophisticated manipulation, and the technical nature of many verification checks demand automated analysis.

How AI Video Verification Works

AI-powered video verification employs multiple complementary analysis techniques. No single method is sufficient on its own — it’s the combination that provides reliable detection.

Temporal Consistency Analysis

AI models analyze the relationship between consecutive frames, looking for inconsistencies that indicate manipulation:

Inter-frame coherence — genuine video has natural, continuous variation between frames. Manipulated sections may show subtle discontinuities: slight jumps in lighting, minor shifts in object position, or changes in noise patterns that break the natural flow.
Motion vector analysis — video compression encodes motion between frames. Manipulated video often has motion vectors that are inconsistent with the visible movement, because the compression was applied after manipulation rather than at the point of original capture.
Optical flow anomalies — the pattern of apparent motion between frames (optical flow) should be physically consistent. AI models can detect flow patterns that violate physical constraints, indicating that frames have been inserted, removed, or altered.

Frequency Domain Analysis

Similar to image analysis but applied per-frame and across frame sequences:

Spectral signatures — AI generation and manipulation tools leave characteristic patterns in the frequency domain of each frame. These signatures persist even when the visual content appears flawless.
Temporal frequency analysis — analyzing how frequency-domain characteristics change over time can reveal manipulation boundaries — the points where genuine footage transitions to altered footage or vice versa.

Audio-Visual Synchronisation

For videos containing speech or environmental audio:

Lip-sync verification — AI models trained on natural speech verify that lip movements precisely match the audio track. Deepfake face replacements and audio dubbing often produce subtle misalignments that automated analysis can detect.
Environmental audio consistency — background sounds should be consistent with the visual environment and should change naturally as the camera moves. Spliced audio from different environments has different acoustic properties (reverberation, ambient noise spectrum, dynamic range).
Voice authentication — Pindrop’s 2025 Voice Intelligence and Security Report documented 2.6 million fraud events involving synthetic audio. AI voice analysis can distinguish between natural human speech and cloned or synthesised voices by examining micro-characteristics in pitch, cadence, and spectral patterns.

Metadata and Container Analysis

Beyond the visual and audio content, the video file itself carries forensic evidence:

Container format verification — the video’s container (MP4, MOV, AVI) and codec (H.264, H.265, VP9) should be consistent with the claimed recording device. A video claiming to be from an iPhone dashcam app but encoded with desktop video editing software is suspicious.
Quantisation parameter analysis — video compression parameters can indicate whether the video has been re-encoded (a common artifact of editing). Genuine camera recordings typically show a single encoding pass; edited video shows signs of multiple passes.
Metadata integrity — creation timestamps, GPS data, device identifiers, and recording parameters should be internally consistent and consistent with the claim details.

Injection Detection

A distinct but critical attack vector: bypassing the camera entirely to inject synthetic video directly into the submission pipeline. This might involve:

Submitting a pre-recorded or generated video through a mobile app as if it were a live capture
Intercepting the video upload and replacing it with manipulated footage
Using virtual camera software to present generated video as a live feed

Injection detection analyses device-level signals, capture-time verification, and pipeline integrity to identify video that wasn’t genuinely captured by the claimed device at the claimed time.

Implementation for Insurers

Where Video Verification Fits in the Claims Workflow

At claims intake. The highest-impact integration point. Video is analyzed automatically when submitted through your mobile app, web portal, or email. Results are available before the claim reaches an adjuster.

During investigation. SIU teams can submit specific videos for deeper analysis when investigating suspicious claims. This on-demand capability supplements the automated intake screening.

For litigation support. When a claim proceeds to legal proceedings, forensic video analysis produces court-admissible documentation of manipulation findings — or confirms the authenticity of genuine evidence.

Integration Requirements

For practical deployment, video verification needs to:

Handle insurance-grade video — compressed smartphone footage, dashcam recordings at various frame rates, surveillance footage from consumer-grade cameras. Not just clean, high-resolution test video.
Process at scale — large insurers receive thousands of video files daily. Analysis must complete within minutes, not hours, to avoid becoming a bottleneck.
Integrate via API — connect to existing claims management platforms (Guidewire, Duck Creek, Majesco) and mobile claims apps without requiring a separate upload workflow.
Produce actionable output — forensic reports with specific findings, confidence levels, and visual annotations that adjusters and investigators can act on.

What to Expect from Results

Video verification produces several categories of output:

Finding	Meaning	Action
Authentic — high confidence	No indicators of manipulation detected	Process claim normally; log verification for audit
Metadata anomaly	File metadata inconsistent with claimed circumstances	Request clarification from claimant; may be benign (e.g., timezone mismatch)
Temporal inconsistency	Frame-level anomalies detected in specific sections	Escalate to SIU with forensic report identifying affected sections
Audio-visual mismatch	Audio doesn’t match visual content	Escalate to SIU; request additional evidence
Generation indicators	Signatures of AI generation detected	Escalate immediately to SIU; preserve original file for forensic evidence

The Road Ahead

Video deepfake technology is advancing faster than image manipulation. Models like OpenAI’s Sora, Runway Gen-3, and their successors are producing increasingly realistic generated video. The window for insurers to implement detection before the technology becomes mainstream is narrowing.

The insurers establishing video verification capabilities now — while the technology is still imperfect and detectable — will have mature, battle-tested systems in place when video deepfakes reach the quality threshold that makes them a routine fraud tool.

Those who wait will face a harder problem: deploying detection after sophisticated video fraud is already embedded in their claims pipeline, with no baseline to distinguish genuine historical claims from fraudulent ones.

deetech’s platform analyses video evidence alongside images, documents, and audio — providing comprehensive media verification for insurance claims. Our forensic output is designed for claims workflows, SIU investigations, and legal proceedings. Request a demo.

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