Why Forensic-Grade Detection Isn't Enough for Insurance Claims

Forensic-grade deepfake detection sounds like exactly what insurers need. The highest possible accuracy. The most sophisticated analysis. The kind of technology that can identify synthetic media with near-certainty in controlled conditions.

There’s one problem: insurance doesn’t operate in controlled conditions.

Insurance operates at scale, under time pressure, across distributed workflows, with claimants who expect fast resolution. A detection approach built for forensic investigation — where a single analyst spends 35 minutes examining a single piece of media — fundamentally cannot serve an industry processing millions of claims per year.

This isn’t an argument against accuracy. It’s an argument for understanding what insurance actually requires.

The Scale Problem

Australia’s general insurance industry processes approximately 4.5 million claims annually, according to the Insurance Council of Australia. The top five US property and casualty insurers each handle between 5 and 15 million claims per year. Globally, the insurance industry processes an estimated 300 million claims annually.

Each claim may contain multiple pieces of media evidence — photographs, video, documents, audio recordings. A single property damage claim typically includes 5 to 15 photographs. A motor vehicle claim might include dashcam footage, photos of vehicle damage, and images of the scene.

Conservative estimate: a mid-sized insurer processes 50,000 to 100,000 individual media files per week that could potentially be AI-generated.

Now consider forensic-grade detection timelines. Academic and specialist forensic tools typically require 15 to 45 minutes per media file for comprehensive analysis. Even at the optimistic end of 15 minutes per file, processing 50,000 files would require 12,500 hours of analyst time per week — roughly 313 full-time analysts dedicated solely to deepfake screening.

That’s not a detection solution. It’s a staffing crisis.

The Speed Problem

Claims processing speed directly impacts customer satisfaction, regulatory compliance, and competitive positioning. The Australian Financial Complaints Authority (AFCA) requires insurers to make claims decisions within specific timeframes. General Insurance Code of Practice signatories must respond to claims within 10 business days.

Claimants expect faster. A 2024 J.D. Power survey found that claims satisfaction drops measurably when initial response takes longer than 24 hours. Insurers investing in digital claims — mobile uploads, automated triage, instant acknowledgment — cannot introduce a detection bottleneck that adds hours or days to processing.

Forensic detection operates on a fundamentally different timescale. When a claimant uploads photos through a mobile app at 2 PM and expects acknowledgment by 3 PM, there is no window for a 35-minute manual forensic review. The claim has already entered the system, been assigned to an adjuster, and begun accruing processing costs.

What speed actually means for detection

Effective detection for insurance claims must operate in seconds, not minutes. Specifically:

Sub-2-second analysis per image at the point of upload
Under 10 seconds for video files up to 60 seconds in length
Real-time scoring that feeds directly into triage workflows
Batch processing capability for surge events delivering thousands of claims simultaneously

This isn’t about cutting corners on analysis. It’s about deploying detection models optimized for the insurance use case — where the cost of a missed detection is a fraudulent payout, but the cost of slow detection is operational paralysis.

The Workflow Integration Problem

Forensic detection tools are built for investigators. They produce detailed technical reports — frequency domain analysis charts, error level analysis overlays, GAN fingerprint probability distributions. These outputs are valuable in a forensic context where the user is a trained analyst building an evidence file.

Insurance claims adjusters are not forensic analysts. They are processing dozens of claims per day across multiple systems. They need a clear signal: is this media suspicious, and what should I do about it?

What adjusters actually need

A traffic light, not a lab report: Green (proceed), amber (review), red (escalate). Confidence scores are useful context, but the primary output must be actionable without specialist interpretation.
Integration with existing platforms: Detection signals need to appear in Guidewire, Duck Creek, Sapiens, or whatever claims management system the insurer uses. A separate forensic tool with its own login, dashboard, and workflow is a tool that won’t get used.
Audit trail automation: When a claim is flagged, the detection result — including technical evidence — must be automatically logged in the claim file. Adjusters shouldn’t need to manually export results from one system and import them to another.
Escalation routing: High-confidence detections should automatically route to the Special Investigations Unit (SIU). Medium-confidence detections should flag for adjuster review with supporting evidence. Low-confidence results should be logged but not interrupt the standard workflow.

Forensic tools provide none of this out of the box. They sit outside the claims workflow, require specialist operation, and produce outputs that need translation before they’re useful to the people actually making claims decisions.

The Cost Problem

Forensic-grade detection carries forensic-grade costs. Specialist tools are typically licensed per-seat or per-analysis, with pricing structures designed for investigation teams processing hundreds of files per month — not operations teams processing hundreds of thousands.

Consider the unit economics. A forensic analysis costing $5 to $25 per media file (accounting for tool licensing, analyst time, and overhead) applied to 50,000 files per week would cost $250,000 to $1.25 million weekly. That’s $13 million to $65 million annually — for a single mid-sized insurer.

Compare this to API-based detection designed for scale. Automated analysis at $0.01 to $0.05 per file applied to the same 50,000 files costs $500 to $2,500 per week, or $26,000 to $130,000 annually.

The economics aren’t close. And the automated approach delivers results in seconds rather than minutes.

The false economy of “accuracy first”

Proponents of forensic-grade detection argue that higher accuracy justifies higher cost. The logic: if forensic analysis catches 99.5% of deepfakes versus 95% for automated detection, the additional 4.5% of caught fraud pays for the investment.

This argument has three flaws:

First, it assumes all deepfakes are equal in cost. Most insurance deepfakes are submitted as part of low-to-mid-value claims. The median fraudulent claim value is significantly lower than the cost of forensic analysis across all claims.

Second, it ignores the deterrence effect. Automated screening that catches 95% of deepfakes and provides instant feedback fundamentally changes the risk calculus for fraudsters. If evidence is screened at upload, the expected success rate for synthetic fraud drops dramatically, deterring attempts regardless of the exact detection rate.

Third, it assumes forensic accuracy translates to operational accuracy. A tool that’s 99.5% accurate in laboratory conditions but can only be applied to 1% of claims (due to speed and cost constraints) catches fewer total deepfakes than a tool that’s 95% accurate but screens 100% of claims.

Applied to 50,000 files: forensic screening of 500 files (1%) at 99.5% accuracy catches 498 deepfakes. Automated screening of 50,000 files at 95% accuracy catches 47,500 deepfakes. Even if the deepfake prevalence rate is only 2%, automated screening catches 950 versus forensic’s 10.

Coverage beats precision when fraud is distributed across your entire claims volume.

The Surge Problem

Natural disasters create claims surges that expose the limitations of forensic approaches most starkly. After the 2022 Eastern Australia floods, insurers received over 230,000 claims. The 2019–2020 bushfire season generated approximately 44,000 claims. Cyclone Reasi in 2024 produced over 28,000 claims in days.

These surge events are precisely when fraudulent claims spike. Fraudsters exploit the chaos, the volume, and the pressure on insurers to process legitimate claims quickly. During the aftermath of Hurricane Ian in 2022, Florida’s Division of Investigative and Forensic Services reported a 35% increase in suspected fraudulent claims compared to non-disaster periods.

Forensic-grade detection cannot scale to meet surge demand. You cannot hire 300 forensic analysts for a two-week period. You cannot queue 230,000 claims for manual review. You cannot tell legitimately affected policyholders to wait while each submitted photo undergoes 35 minutes of analysis.

Automated detection scales linearly with compute. Surge capacity is a configuration change, not a hiring decision. Batch analysis of surge claims enables pattern detection that forensic analysis of individual files would miss entirely.

What Insurance Actually Needs

The insurance deepfake detection problem has specific requirements that diverge from forensic analysis:

1. Triage-first architecture

Detection should function as a triage layer, not a final determination. The goal at the point of claim submission is not to conclusively prove a photo is synthetic — it’s to identify which of the 50,000 files this week warrant closer examination. A 95% accurate triage layer that reduces the investigation pool from 50,000 to 500 is transformatively valuable, even if those 500 then require deeper analysis.

2. Insurance-specific training

Generic deepfake detection models are trained primarily on face-swap and face-generation scenarios — the most common deepfake use cases in media and politics. Insurance fraud involves different content: property damage, vehicle damage, environmental conditions, medical documentation. Detection models must be trained on insurance-relevant synthetic content, not just faces.

3. Continuous retraining

Generative AI models improve on monthly release cycles. A detection model trained on Stable Diffusion 1.5 outputs will underperform against SDXL, which will underperform against SD 3.0, which will underperform against whatever ships next quarter. Continuous model updates are not optional — they’re the core of the product.

4. Evidence preservation

When a deepfake is detected, the insurer needs an evidence package suitable for claim denial, regulatory reporting, and potential law enforcement referral. This package must be generated automatically, not manually assembled by a forensic analyst. It should include the original submitted file, detection confidence scores and contributing factors, technical analysis summary in plain language, chain of custody documentation, and timestamps and system logs.

5. Feedback loops

Detection improves when it learns from outcomes. Claims that were flagged as suspicious but turned out to be legitimate (false positives) should feed back into model training. Claims that were paid but later identified as fraudulent (false negatives) are even more valuable. This feedback loop requires integration with claims management systems — another area where standalone forensic tools fall short.

The Right Role for Forensic Analysis

None of this means forensic-grade detection has no role in insurance. It does — but as a second-tier capability, not the primary screen.

The optimal architecture layers detection:

Tier 1 — Automated screening: Every piece of submitted media analyzed in real time. Sub-second response. High throughput. Moderate-to-high accuracy. Fully integrated into claims workflow.
Tier 2 — Enhanced analysis: Claims flagged by Tier 1 receive deeper automated analysis. Multiple detection models, metadata forensics, cross-claim correlation. Minutes, not seconds. Applied to perhaps 2–5% of total claims.
Tier 3 — Forensic investigation: Claims escalated to SIU receive full forensic treatment. Expert analysis, detailed reporting, evidence preparation for legal proceedings. Hours to days. Applied to perhaps 0.1–0.5% of total claims.

Forensic-grade detection is the right tool for Tier 3. It’s the wrong tool for Tier 1. And Tier 1 is where the vast majority of deepfake fraud will be caught or missed.

Conclusion

The insurance industry doesn’t need the world’s most accurate deepfake detector applied to a handful of claims. It needs a fast, scalable, integrated detection capability applied to every claim.

Forensic-grade accuracy in a vacuum is impressive. Forensic-grade accuracy applied to 1% of your claims volume while the other 99% passes unscreened is a vulnerability.

The question isn’t “how accurate is your detection?” It’s “how many claims does your detection actually cover?” For most insurers today, the honest answer is close to zero.

That’s the gap that needs closing — and forensic-grade tools, by design, cannot close it.

To learn how deetech helps insurers detect deepfake fraud with purpose-built AI detection, visit our solutions page or request a demo.