Insurance Fraud in the Age of Generative AI: A Board-Level Briefing

Audience: Board directors, CROs, CFOs, CEOs, and senior executives Purpose: Risk quantification and investment case for AI media authenticity detection Reading time: 10 minutes

Executive Summary

Generative artificial intelligence has fundamentally changed the economics of insurance fraud. Tools that produce convincing fake photographs, documents, video evidence, and voice recordings are now freely available, require no technical skill, and operate in seconds.

The insurance industry’s current fraud detection infrastructure — built to identify suspicious data patterns in claims records — does not examine the media submitted as evidence. This creates a systemic vulnerability that is being exploited today and will accelerate.

This briefing quantifies the risk, outlines the competitive implications of inaction, and presents the investment case for AI-powered media authenticity detection.

The Threat: What Has Changed

The democratisation of fraud tools

Before 2023, creating convincing fake photographic evidence required specialist skills — Photoshop expertise, knowledge of lighting and perspective, hours of careful manipulation. The barrier to entry was high, limiting media-based fraud to sophisticated operators.

Today:

Image generation — Stable Diffusion, Midjourney, DALL-E, and Flux produce photorealistic images from text descriptions. “Photo of hail damage on a white Toyota Camry in a suburban driveway” generates convincing evidence in under 10 seconds.
Image manipulation — AI inpainting tools seamlessly edit genuine photographs. Minor fender damage can be visually escalated to major structural damage with a few clicks.
Document generation — Large language models produce convincing repair quotes, medical certificates, and invoices with correct formatting, plausible amounts, and appropriate terminology.
Voice cloning — Consumer services clone a voice from a few seconds of sample audio. Phone-based claims submissions and identity verification are vulnerable.
Video generation — While still emerging, AI video generation is progressing toward the point where fabricated dashboard camera footage becomes feasible.

These tools are free or nearly free. They require no technical training. Tutorials for fraud-specific applications circulate openly online.

Scale of the problem

245% increase in deepfake-related fraud globally between 2023 and 2024 (Sumsub 2024 Identity Fraud Report)
US$40 billion estimated losses from generative AI-enabled fraud across financial services by 2027 (Deloitte, 2024)
A$2.2 billion annual cost of insurance fraud in Australia (Insurance Council of Australia)
4,700% increase in deepfake content detected online between 2019 and 2024 (World Economic Forum, citing industry data)
1 in 4 financial services firms globally reported encountering deepfake fraud in 2024 (Regula, 2024 survey)

The precise volume of AI-generated evidence in insurance claims is unknown — because existing systems cannot detect it. This is the core problem: the threat is growing in an unmonitored space.

Risk Quantification

Direct financial exposure

Model assumptions (mid-size Australian insurer):

Annual claims volume: 200,000
Average claim value: A$12,000
Total claims expenditure: A$2.4 billion
Current estimated fraud rate: 10% of claims by volume (industry consensus range: 5-15%)
Estimated AI-enabled fraud as percentage of total fraud: 5-10% in 2026, projected 20-30% by 2028

Estimated annual exposure to AI-enabled fraud:

Scenario	AI fraud rate	Estimated losses
Conservative (2026)	5% of fraudulent claims	A$12-24 million
Moderate (2027)	15% of fraudulent claims	A$36-72 million
Aggressive (2028)	30% of fraudulent claims	A$72-144 million

These figures represent losses from claims where AI-generated or manipulated evidence was a contributing factor in the fraudulent claim succeeding. They do not include investigation costs, legal expenses, or indirect impacts.

Claim inflation exposure

The most likely near-term application of generative AI in insurance fraud is not entirely fabricated claims — it is claim inflation. A genuine incident with genuine damage, where AI-manipulated photos exaggerate the extent of damage to inflate the payout.

This is particularly insidious because:

The claim has a legitimate basis (making pattern-based fraud detection less likely to flag it)
The inflated amount may fall within normal ranges for the claim type
The photos appear to corroborate the inflated amount
Without media authenticity verification, there is no mechanism to challenge the visual evidence

If AI-enabled claim inflation averages 30-50% above genuine damage values, even a small percentage of inflated claims creates material financial impact across a portfolio.

Catastrophe event amplification

Catastrophe events — floods, cyclones, bushfires, severe storms — create conditions that amplify AI-enabled fraud risk:

Processing pressure reduces per-claim scrutiny
Legitimate damage patterns provide cover for fabricated evidence
Volume surge overwhelms investigation capacity
Public sympathy creates reluctance to challenge claims
Similar damage across claims makes recycled imagery harder to spot

The 2022 Australian floods generated over 230,000 claims. The 2024 events produced similar volumes. In these conditions, the incremental cost of AI-enabled fraud is amplified by the sheer volume of claims processed under time pressure.

The Detection Gap

What current systems see

Most Australian insurers deploy some combination of:

Pattern-based fraud detection (e.g., Shift Technology, FRISS) — analyses structured claims data for suspicious patterns
Rules-based flags — predefined criteria that trigger investigation referrals
SIU investigation — human investigators examining flagged claims
Adjuster judgment — experienced claims handlers identifying suspicious submissions

These systems examine claims data: amounts, dates, claimant history, provider networks, description consistency.

What current systems cannot see

None of the above systems examine the actual media submitted as evidence:

Photos of damage are not analyzed for AI generation
Documents are checked for content consistency, not for whether they were AI-generated
Video evidence is viewed by humans who cannot reliably detect deepfakes
Voice recordings are not checked for cloning

The gap is architectural. Data-pattern fraud detection and media authenticity verification are different technical capabilities requiring different technologies.

A study published in Psychological Science (Nightingale & Farid, 2023) found that human evaluators could not reliably distinguish AI-generated images from real photographs — and in many cases rated AI-generated images as more authentic. Relying on human visual inspection to catch AI-generated evidence is not a viable strategy.

Regulatory Trajectory

Current regulatory posture

Australian prudential and conduct regulators have not yet issued specific mandates for deepfake detection in insurance. However, the regulatory trajectory is clear:

APRA CPS 234 (Information Security) requires entities to maintain information security commensurate with threats. As AI-generated fraud becomes a recognized threat, the expectation of controls will extend to media verification.
APRA SPS 220 (Risk Management) requires sound risk management frameworks covering operational risks, including fraud.
ASIC has published guidance on AI governance in financial services and is actively monitoring AI-related risks.
Privacy Act 1988 reforms (in progress) include AI-specific provisions that may affect how automated detection systems must be governed.
General Insurance Code of Practice requires fair and timely claims handling, which implies adequate fraud controls to protect the pool of policyholders.

International precedent

The EU AI Act (effective 2025-2026) classifies certain AI detection systems and imposes transparency requirements.
UK FCA has issued guidance on AI risk management in financial services.
US state regulators are developing AI-specific insurance regulations, with several states requiring disclosure of AI use in claims handling.

The pattern across jurisdictions is toward explicit regulatory expectations for managing AI-related risks. Carriers that implement media authenticity controls proactively will be ahead of regulatory mandates, not scrambling to comply.

Competitive Implications

First-mover advantage

The insurance industry operates on trust and pricing discipline. Carriers that detect and prevent AI-enabled fraud have a structural advantage:

Lower loss ratios — catching fraudulent claims that competitors miss directly improves combined ratios
Pricing advantage — lower fraud losses enable more competitive pricing without sacrificing profitability
Reinsurance terms — demonstrating AI fraud controls may improve reinsurance pricing as reinsurers become aware of the generative AI fraud risk
Regulatory standing — proactive risk management positions carriers favourably with regulators

Second-mover penalty

Carriers that delay face compounding disadvantages:

Adverse selection — as some carriers implement detection, fraudsters migrate to carriers without it. The unprotected carrier becomes the target of choice.
Accumulating losses — every quarter without detection is a quarter of undetected AI-enabled fraud adding to the loss ratio
Implementation under pressure — deploying detection technology reactively (after a significant loss event or regulatory mandate) is more expensive and less effective than planned implementation
Talent and capability gap — internal expertise in AI fraud detection takes time to develop

The portfolio effect

Even if AI-enabled fraud represents a small percentage of current claims, the portfolio impact is material. A 1% increase in the fraud rate across a A$2.4 billion claims portfolio is A$24 million. If that 1% is concentrated in lines with higher average claim values (motor total losses, property claims), the impact per claim is significant.

The Investment Case

What is required

Deploying media authenticity detection for insurance claims requires:

Technology platform — AI-powered detection system designed for insurance claims media
Integration — connection with existing claims management systems
Process adaptation — updated claims handling procedures incorporating detection findings
Training — adjusters and SIU teams trained on interpreting and acting on detection results
Governance — oversight framework for automated detection decisions

Cost framework

Implementation costs:

Platform licensing: per-claim pricing, scaled to claims volume
Integration: 4-8 weeks of IT resource for standard claims system integration
Training: 1-2 days for claims teams, 2-3 days for SIU teams
Governance: incorporated into existing fraud and technology governance frameworks

Ongoing costs:

Per-claim detection fees (volume-tiered)
Annual model updates (typically included in licensing)
Internal administration (minimal — system operates automatically)

Return on investment

Conservative model:

Parameter	Value
Annual claims	200,000
Claims screened	200,000 (100%)
AI-enabled fraud detected	200-500 claims (0.1-0.25% of total)
Average prevented loss per claim	A$15,000
Annual fraud prevented	A$3-7.5 million
Annual detection cost	A$400,000-800,000
Net annual benefit	A$2.2-6.7 million
ROI	5-9x

This model is conservative. It assumes AI-enabled fraud represents only 0.1-0.25% of claims — well below industry estimates. It also excludes:

Deterrence effect (fraudsters avoiding carriers with known detection capability)
Claim inflation detection (partial fraud on genuine claims)
Recovery from historical claims identified through batch analysis
Reinsurance benefit from improved fraud controls
Regulatory risk reduction value

Payback period

At the conservative model parameters, the investment pays back within the first quarter of operation. Even if detection rates are half the conservative estimate, payback occurs within the first year.

Recommended Actions

Immediate (0-3 months)

Threat assessment — commission an internal review of your current claims media submission processes and identify where AI-generated evidence could enter your workflow undetected
Technology evaluation — assess available deepfake detection tools for insurance against your specific requirements
Board reporting — include AI-enabled fraud as a standing item in risk committee reporting

Short-term (3-6 months)

Pilot program — deploy media authenticity detection on a specific line of business (motor or property recommended) to quantify exposure and validate detection effectiveness
Process design — develop claims handling procedures that incorporate detection findings, including escalation paths and decision frameworks
Regulatory engagement — brief APRA/ASIC relationship managers on your approach to AI-enabled fraud risk

Medium-term (6-12 months)

Full deployment — roll out media authenticity detection across all claims lines based on pilot findings
Historical analysis — batch analyze historical claims media to identify potentially fraudulent approved claims
Reinsurance discussion — present AI fraud detection capability to reinsurers as part of treaty renewal discussions
Industry collaboration — engage with the Insurance Council of Australia and industry bodies on shared intelligence regarding AI-enabled fraud trends

Key Questions for the Board

What is our current capability to detect AI-generated evidence in claims? If the answer is “adjuster visual inspection,” the capability is inadequate.
What is our estimated annual exposure to AI-enabled fraud? Use the quantification framework above with your actual claims data.
Are we a target of preference for AI-enabled fraudsters? If competitors implement detection and you do not, you become the path of least resistance.
What is the regulatory trajectory? Regulators are moving toward explicit expectations for AI risk management. Are we ahead of or behind this curve?
What does the investment case look like with our actual data? The conservative model above can be calibrated with your specific claims volume, average values, and estimated fraud rates.

Conclusion

Generative AI has changed the fraud threat landscape for insurance permanently. The tools are available, the capability is accessible, and the trend is toward more convincing and more common AI-generated evidence in claims.

Current fraud detection systems were not designed for this threat. They analyze data patterns, not media authenticity. The gap is real, measurable, and growing.

The investment case for media authenticity detection is strong at current threat levels and becomes more compelling each quarter as generative AI capabilities advance. Early adopters gain both protective benefit (reduced fraud losses) and competitive advantage (better loss ratios, regulatory positioning, reinsurance terms).

The question for the board is not whether to invest in this capability, but how quickly.

For technical detail on detection capabilities, see the deepfake detection FAQ for insurance companies. For an overview of the current threat landscape, see The State of Deepfake Fraud in Insurance 2026.

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