Deetech vs Shift Technology: AI Fraud Detection Compared

Shift Technology is one of the most established names in insurance fraud detection. Their AI-powered platform analyses claims data to identify suspicious patterns, score fraud risk, and automate investigation triage. Over 100 insurance carriers worldwide rely on Shift for fraud detection.

Deetech detects AI-generated and manipulated media in insurance claims — deepfake photos, AI-generated documents, manipulated video evidence, and cloned voice recordings.

These are not competing products. They are complementary technologies addressing different fraud vectors. But the distinction matters, because many insurers assume Shift Technology (or similar pattern-based fraud tools) can detect deepfakes. They cannot.

What Shift Technology Does Well

Shift Technology deserves its market position. Their platform is genuinely effective at what it does:

Claims pattern analysis. Shift’s AI examines structured claims data — dates, amounts, claimant history, provider networks, claim descriptions — to identify patterns consistent with fraud. This catches fraud rings, serial claimants, inflated claims, and staged incidents.
Network analysis. Identifying connections between claimants, witnesses, service providers, and claims handlers that suggest coordinated fraud.
Fraud scoring. Every claim receives a risk score based on hundreds of data points, allowing SIU teams to prioritize investigations.
Subrogation identification. Detecting claims where recovery from third parties is possible.
Claims automation. Routing low-risk claims for straight-through processing while escalating suspicious claims for human review.
Proven at scale. Deployed across major global carriers with years of production data validating their models.

Shift’s technology analyses what’s in the claims record: the structured data, the text descriptions, the claimant profiles. It does this well, and most carriers using Shift see meaningful improvements in fraud detection rates and SIU efficiency.

What Shift Technology Cannot Do

Shift Technology cannot examine the media attached to a claim and determine whether it is authentic.

This is not a product gap that Shift is likely to close. It’s a fundamentally different technical capability:

AI-generated photos. A claimant submits photos of vehicle damage generated by Stable Diffusion, DALL-E, or Midjourney. The structured claims data looks normal — correct policy details, reasonable damage description, consistent dates. Shift scores the claim based on data patterns. The AI-generated photos pass through unexamined.
Manipulated video evidence. Dashboard camera footage edited to change timestamps, alter license plates, or fabricate an accident sequence. Shift analyses the claims data, not the video content.
Voice cloning. A fraudster uses voice cloning to impersonate a policyholder during a phone claim, bypassing voice verification. Shift’s pattern analysis operates on structured data, not audio authentication.
Doctored documents. AI-generated repair quotes, medical certificates, or invoices that look authentic but contain fabricated information. Shift may flag inconsistencies in the amounts or providers, but cannot determine whether the document itself was AI-generated.
Recycled imagery. Photos of genuine damage from a previous incident or sourced from the internet, submitted as evidence for a new claim. Without reverse image analysis and media forensics, these pass through pattern-based systems undetected.

The core issue is architectural: Shift Technology analyses structured data and text. It does not perform computer vision analysis on claims media. These are different technical domains requiring different models, different training data, and different detection approaches.

This blind spot is becoming increasingly dangerous. According to Sumsub’s 2024 Identity Fraud Report, deepfake-related fraud increased 245% between 2023 and 2024 globally. The Insurance Fraud Bureau (UK) has documented cases of AI-generated evidence in claims submissions. Deloitte’s 2024 report estimated that generative AI-enabled fraud could reach US$40 billion in losses by 2027.

The state of deepfake fraud in insurance is evolving rapidly. Generative AI tools that produce convincing fake imagery are now freely available, require no technical expertise, and can produce outputs in seconds.

A pattern-based fraud detection system that examines only structured claims data cannot address this threat vector. It was never designed to.

Consider a concrete scenario:

A legitimate policyholder makes a genuine motor claim.
They submit AI-generated photos showing more extensive damage than actually occurred, inflating the claim value.
The claim data is internally consistent: the policy is valid, the incident date is plausible, the damage description matches the photos.
Shift Technology scores the claim as low-risk based on data patterns.
The inflated claim is approved.

This isn’t hypothetical. It’s the emerging fraud playbook. And it exploits exactly the gap between data-pattern fraud detection and media authenticity verification.

Where Deetech Fits

Deetech operates at the media layer — the photos, videos, audio recordings, and documents that accompany claims. It answers a question that Shift Technology does not ask: is this media authentic?

Deetech’s detection capabilities:

AI-generated image detection. Identifying images produced by diffusion models (Stable Diffusion, Midjourney, DALL-E, Flux), GANs, and other generative architectures. Detection covers the specific artifact patterns, frequency domain anomalies, and statistical signatures that distinguish AI-generated imagery from genuine photographs.
Image manipulation detection. Identifying edits to genuine photos: splicing, cloning, inpainting, content-aware fill, and other modifications that alter the content of an authentic image.
Video analysis. Frame-by-frame analysis of video evidence for temporal inconsistencies, deepfake face swaps, and editing artifacts.
Audio verification. Detecting voice cloning and audio manipulation in recorded statements and phone claims.
Document authenticity. Analyzing submitted documents for AI generation signatures, including repair quotes, medical certificates, and invoices.
Reverse image matching. Checking submitted photos against databases of known fraud imagery and publicly available images.

Complementary, Not Competitive

The correct architecture for modern insurance fraud detection is not Deetech or Shift Technology. It’s Deetech and a pattern-based fraud detection platform.

Shift Technology analyses structured claims data to identify suspicious patterns, score fraud risk, and prioritize investigations. This catches traditional fraud: padding, staging, misrepresentation based on data patterns.

Deetech analyses claims media to verify authenticity. This catches AI-enabled fraud: deepfake photos, manipulated evidence, AI-generated documents, and voice cloning.

Together, they cover both the data layer and the media layer. Neither alone is sufficient.

How They Work Together

In a well-architected fraud detection stack:

Claim submitted with structured data and media attachments.
Shift Technology (or equivalent) scores the claim based on data patterns, claimant history, and network analysis.
Deetech simultaneously analyses all attached media for authenticity.
Combined risk assessment — a claim might score low-risk on data patterns (Shift) but high-risk on media authenticity (Deetech), or vice versa.
Investigation prioritisation based on combined signals from both systems.

This combined approach catches fraud that either system alone would miss:

Shift catches, Deetech misses: A fraud ring using genuine photos but fabricated claims data. Pattern analysis identifies the network; media analysis finds nothing unusual because the media is authentic.
Deetech catches, Shift misses: An otherwise legitimate-looking claim with AI-generated damage photos. Data patterns appear normal; media analysis flags the synthetic content.
Both catch: A sophisticated attempt with both suspicious data patterns and manipulated media. The combined signal is stronger than either alone.

The Integration Question

For carriers already running Shift Technology, adding Deetech does not require replacing or reconfiguring their existing fraud stack. Deetech operates as an additional detection layer:

API integration — Deetech’s REST API can be called alongside Shift’s analysis, with results merged in the carrier’s claims management system or fraud investigation platform.
Independent operation — Deetech analyses media independently of Shift’s data analysis. No data sharing between platforms is required for either to function.
Combined dashboards — For carriers using investigation management platforms (e.g., SIU case management systems), both Shift’s fraud scores and Deetech’s media authenticity assessments can feed into the same case view.
Claims system integration — Deetech integrates with the same claims platforms (Guidewire, Duck Creek, Sapiens) that Shift connects to, enabling a unified workflow.

The implementation is additive. Carriers don’t need to choose between protecting against traditional fraud patterns and protecting against AI-generated evidence. They need both.

What Insurance Carriers Should Ask

When evaluating your fraud detection stack, these questions reveal whether you have a deepfake blind spot:

Can your current fraud tools analyze the photos submitted with claims? If your fraud detection operates only on structured data and text, the answer is no.
Can your tools detect AI-generated images? Pattern-based systems like Shift analyze claims data, not image content.
What happens when a fraudulent claim has perfectly normal data patterns but fake photos? If your system scores it as low-risk, you have a gap.
How do you verify the authenticity of documents submitted with claims? Checking amounts and provider details is different from verifying the document itself wasn’t AI-generated.
Do you have any media verification capability for voice claims? Voice cloning technology is accessible and improving rapidly.

If the answers reveal gaps — and for most carriers they will — the question is not whether to add media authenticity detection, but how quickly.

The financial impact of undetected deepfake fraud compounds over time:

Direct losses from approved fraudulent claims with AI-generated evidence.
Precedent setting — each successful deepfake fraud teaches fraudsters (and their networks) that the technique works.
Escalating sophistication — generative AI improves continuously. The deepfakes of 2026 are significantly more convincing than those of 2024.
Regulatory risk — as regulators become aware of AI-enabled fraud, carriers without media verification may face scrutiny for inadequate fraud controls.

The board-level briefing on generative AI fraud quantifies this risk in terms that CROs and CFOs can action.

Conclusion

Shift Technology is an excellent product for pattern-based fraud detection. It deserves its market position and the trust of the carriers that deploy it.

But it was not designed to detect deepfakes. It does not analyze media. It cannot identify AI-generated photos, manipulated video, cloned voices, or synthetic documents.

Deetech was designed specifically for this purpose. The two platforms serve different functions in the fraud detection stack, and both are necessary for comprehensive protection against modern insurance fraud.

If you’re running Shift Technology (or FRISS, or another pattern-based fraud tool), you already have the data layer covered. Deetech adds the media layer. Together, they close the deepfake blind spot that AI-enabled fraudsters are already exploiting.

For more on how Deetech compares with other detection tools, see Deetech vs FRISS and the top deepfake detection tools for insurance.

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