Why Forensic-Only Workflows Aren't Enough for Insurance Claims

Forensic analysis sounds like exactly what insurers need to fight deepfakes: the most thorough examination, the most detailed evidence, an expert who can explain every finding.

There’s one problem: a workflow that relies on forensic analysis alone doesn’t scale to insurance.

Insurance operates at volume, under time pressure, across distributed workflows, with claimants who expect fast resolution. A process in which a specialist examines each piece of media by hand, often for many minutes per file, cannot cover the millions of photos, documents and recordings that large insurers receive every year.

This isn’t an argument against forensic depth. It’s an argument for putting it in the right place: behind an automated screening layer that looks at everything first.

The Scale Problem

Large insurers handle millions of claims a year, and each claim may contain multiple pieces of media: photographs, video, documents, audio recordings. A single property damage claim often includes a dozen or more photos. A motor claim might include dashcam footage, photos of vehicle damage, and images of the scene.

Consider an illustrative mid-sized insurer receiving 50,000 media files a week (an assumption for this example, not a measured figure). If manual forensic review took 15 minutes per file, covering every file would take 12,500 analyst-hours a week, roughly 313 full-time analysts doing nothing but deepfake screening.

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

The Speed Problem

Claims handling speed affects customer satisfaction, regulatory obligations and competitive position. In Australia, for example, the General Insurance Code of Practice requires insurers to decide most claims within four months of receiving them, and within 10 business days once they have all the information they need. Other markets have their own timeframes.

Claimants expect faster than that. Insurers investing in digital claims (mobile uploads, automated triage, instant acknowledgment) cannot introduce a detection bottleneck that adds hours or days to every claim.

A forensic-only workflow runs on a different clock. When a claimant uploads photos through a mobile app in the afternoon and expects acknowledgment within the hour, there is no window for a manual forensic review of every image. 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 must fit the claims workflow, but no single latency applies to every media type. A realistic integration plans for:

Image analysis often measured in seconds, depending on file size and analysis depth
Video and large-document analysis measured in minutes where deeper or longer processing is required
Asynchronous results for work that should not block claims intake
Bounded batch requests: up to 20 files per request on non-Enterprise plans and 100 on Enterprise, for images, audio and documents; larger backlogs are submitted as multiple requests, and video is analyzed individually

This isn’t about cutting corners on analysis. It’s about matching the depth of analysis to the stage of the claim, 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 tools are built for investigators. They produce detailed technical outputs: frequency domain charts, error level analysis overlays, compression-history reports. These outputs are valuable where the user is a trained analyst building an evidence file.

Claims adjusters are not forensic analysts. They process dozens of claims a 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.
Results in the systems they already use: Detection signals need to reach Guidewire, Duck Creek, Sapiens, or whatever claims management system the insurer runs, typically through an API and webhook integration. A separate tool with its own login and workflow is a tool that won’t get used.
Automatic record-keeping: When a claim is flagged, the detection result, including its supporting evidence, should be logged against the claim automatically. Adjusters shouldn’t have to export results from one system and import them into another.
Escalation routing: High-confidence detections should route to the Special Investigations Unit (SIU). Medium-confidence detections should flag for adjuster review with supporting evidence. Low-confidence results should be logged without interrupting the standard workflow.

A forensic-only workflow offers little of this out of the box. It sits outside the claims process, requires specialist operation, and produces outputs that need translation before they help the people making claims decisions.

The Cost Problem

Specialist forensic work carries specialist costs. Tools and expert time are typically priced per seat, per analysis or per hour, for investigation teams handling hundreds of files a month, not operations teams handling hundreds of thousands.

An illustrative model (assumptions, not measured deetech outcomes or vendor quotes): if a forensic review cost US$5 to US$25 per file once tool licensing, analyst time and overhead are included, reviewing all 50,000 weekly files would cost US$250,000 to US$1.25 million a week, or roughly US$13 million to US$65 million a year for a single mid-sized insurer.

Automated screening costs a small fraction of that per file, which is what makes screening every file feasible. It also removes most of the manual delay: images often return in seconds, while video, large documents, and deeper workflows can take minutes.

The false economy of “accuracy first”

The argument for forensic-only workflows is that deeper analysis is more accurate, so it’s worth the cost. Suppose, for illustration, that forensic review catches 99.5% of deepfakes it examines and automated screening catches 95%.

That argument has three flaws:

First, most deepfake fraud is likely to arrive in low-to-mid-value claims. Spending more on analysis than a claim is worth doesn’t pay off, however accurate the analysis.

Second, it ignores deterrence. If every piece of evidence is screened at upload, the expected payoff from synthetic evidence drops, which discourages attempts regardless of the exact detection rate.

Third, it confuses accuracy with coverage. A review that is 99.5% accurate but can only be applied to 1% of files catches fewer deepfakes than a screen that is 95% accurate and covers every file.

Using the same illustrative 50,000 files and assuming 2% are deepfakes (1,000 files): forensic review of a random 1% sample (500 files, about 10 of them deepfakes) catches about 10. Automated screening of all 50,000 files at 95% catches about 950.

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

The Surge Problem

Natural disasters create claims surges that expose the limits of forensic-only approaches most starkly. The 2022 floods in Queensland and New South Wales generated about 246,000 claims, according to the Insurance Council of Australia, and the 2019–20 Black Summer bushfires about 38,000. US hurricanes and European floods produce similar spikes.

Surge events are when fraudulent claims tend to spike. Fraudsters exploit the chaos, the volume, and the pressure on insurers to pay legitimate claims quickly.

A forensic-only workflow cannot scale to meet that demand. You cannot hire hundreds of forensic analysts for a two-week period, queue a quarter of a million claims for manual review, or ask affected policyholders to wait while every photo is examined by hand.

Automated screening can be planned for volume: capacity is agreed and scheduled rather than hired. Analyzing surge claims in batches can also surface patterns, such as the same image appearing in several claims, that reviewing files one at a time would miss.

What Insurance Actually Needs

Insurance deepfake detection has requirements that differ from forensic analysis:

1. Triage-first architecture

Detection should work as a triage layer, not a final determination. The goal at submission is not to prove conclusively that a photo is synthetic; it’s to identify which of this week’s files warrant closer examination. A screening layer that narrows 50,000 files to a few hundred for deeper review is hugely valuable, even though those few hundred then need more work.

2. Insurance-relevant evaluation

Generic deepfake detection has historically focused on face-swap and face-generation scenarios, the most common use cases in media and politics. Insurance fraud involves different content: property damage, vehicle damage, environmental conditions, documents. Detection should be tested on insurance-relevant synthetic content, not just faces, and evaluated on each insurer’s representative media.

3. Regular updates

Generative AI improves on short release cycles. A detector that handled last year’s image models well may struggle with this year’s. Keeping up with new generators is a core part of the product, not an optional extra.

4. Evidence preservation

When a deepfake is detected, the insurer needs a record suitable for claim review, regulatory reporting, and potential law enforcement referral. It should be generated automatically, not assembled by hand: the original submitted file, detection confidence and contributing factors, a plain-language summary of the analysis, and timestamps and system logs that support the insurer’s own chain-of-custody process.

5. Feedback loops

Detection improves when outcomes are measured. Claims flagged as suspicious that turned out to be legitimate (false positives) should inform threshold settings. Claims paid and later found to be fraudulent (false negatives) are even more valuable for re-evaluation. This requires connecting detection results with claims outcomes, another area where standalone forensic tools fall short.

The Right Role for Forensic Analysis

None of this means forensic analysis has no role in insurance. It does, as a second- or third-tier capability rather than the primary screen.

A layered architecture looks like this:

Tier 1: Automated screening. Every piece of submitted media analyzed on arrival. Images often return in seconds; longer media runs asynchronously. High throughput, integrated into the claims workflow.
Tier 2: Enhanced analysis. Claims flagged by Tier 1 receive deeper automated analysis: additional detection passes, metadata forensics, provenance and reverse image checks. Typically minutes rather than hours, applied to a small share of claims.
Tier 3: Forensic investigation. Claims escalated to the SIU receive full forensic treatment: expert analysis, detailed reporting, preparation for any legal proceedings. Hours to days, applied to a very small share of claims.

Forensic depth is the right tool for Tier 3. It’s the wrong tool for Tier 1. And Tier 1 is where most deepfake fraud will be caught or missed.

Conclusion

The insurance industry doesn’t need the world’s most thorough analysis applied to a handful of claims. It needs fast, scalable, integrated screening applied to every claim, with forensic depth reserved for the cases that earn it.

Deep analysis of 1% of your claims volume, while the other 99% passes unscreened, is a vulnerability.

The question isn’t only “how accurate is your detection?” It’s “how much of your claims media does your detection actually cover?” For many insurers today, the honest answer is very little.

That’s the gap that needs closing, and a forensic-only workflow, by design, cannot close it.

To see how deetech™ helps insurers and claims teams screen every claim’s media and escalate the right cases to SIU, visit our solutions page or request a demo.