Introduction
Deepfake technology — AI-generated images, video, audio, and documents that appear genuine — has progressed rapidly in recent years. For insurers, this creates new risks across the policy and claim lifecycle, from synthetic identity creation to fabricated claim evidence.
The ASIC October 2024 REP 798 on AI governance and APRA’s 30 April 2026 industry letter both reflect heightened regulatory focus on AI-related risks, including those associated with synthetic content.¹,³
What Deepfakes Mean (Plain English)
Deepfakes are pieces of media that have been generated or manipulated by AI to appear genuine. They are no longer limited to simple image edits — modern deepfakes can produce convincing video, voice cloning, and even realistic synthetic documents.
In insurance, deepfakes can take several forms:
- Synthetic photographs of damage, injury, or property loss
- Manipulated invoices, receipts, or medical records
- AI-generated identity documents used in application fraud
- Cloned voices used in social engineering attacks
- Synthetic video used to support fabricated claim narratives
Why Deepfakes Are a Growing Concern
Three factors combine to make deepfakes increasingly significant for insurers:
- Capability — generative AI tools are now widely available and produce highly convincing output
- Cost — creating a deepfake no longer requires specialist skills or expensive tools
- Scale — deepfakes can be produced rapidly, enabling industrial-scale fraud activity
ASIC has specifically warned that frontier AI models are accelerating cyber capability, increasing the speed and scale of attacks, and enabling forms of exploitation that were previously out of reach for many actors.²
Common Attack Patterns
Deepfake-enabled fraud against insurers tends to follow several patterns:
- Synthetic identity creation at policy inception, supported by AI-generated documents
- Submission of fabricated claim evidence including photographs and videos
- Social engineering of staff using cloned voices or AI-generated correspondence
- Manipulation of supplier-provided evidence to support inflated claims
- Synthetic medical or repair documentation supporting fabricated losses
Detection Approaches
Detecting deepfakes is itself an AI challenge. Effective approaches typically combine several techniques:
- Forensic analysis of images and documents for signs of manipulation
- Metadata analysis identifying inconsistencies in file properties
- Cross-referencing claim evidence against external data sources
- Behavioural analysis identifying unusual customer interaction patterns
- Specialist tools designed specifically to detect synthetic content
The Australian Regulatory Context
Deepfake risks intersect with several existing Australian regulatory frameworks. APRA’s CPS 230 covers operational risks including those from AI-enabled threats. ASIC’s conduct obligations require firms to manage risks that affect customers. The Privacy Act 1988 governs handling of personal information, including biometric data used in identity verification.³,⁵
APRA and ASIC have both signalled that AI-related risks — including deepfakes and synthetic content — are a present-day supervisory priority requiring active management under existing prudential and conduct standards.¹,³
Building Resilience
Insurers can build resilience against deepfake threats through several measures:
- Layered identity verification combining document, biometric, and behavioural signals
- Robust supplier governance reducing exposure to compromised channels
- Staff training on social engineering and synthetic content
- Investment in detection tools alongside generative AI capabilities
- Clear governance frameworks for AI-enabled risks across the business
Looking Ahead
Deepfake capabilities will continue to evolve, and so must defences. The insurers most likely to manage this challenge effectively are those who treat it not as a single project but as an ongoing capability — combining technology, governance, training, and intelligence sharing.
Industry-level intelligence, including the work of the IFBA and the ICA’s emerging industry-wide fraud detection capability, will play an increasingly important role in identifying and disrupting deepfake-enabled fraud across the Australian market.⁴
Related Topics
Know Your Customer (KYC) in insurance
Claims fraud: detection, investigation, and prevention
Sources & further reading
¹ ASIC — Report 798: Beware the gap — governance arrangements in the face of AI innovation (October 2024)
² ASIC — Letter to AFS licensees on AI-related cyber risks (2026)
³ APRA — Letter to Industry on Artificial Intelligence (30 April 2026)
⁴ Australian Government — Voluntary AI Safety Standard and National AI Plan
⁵ Privacy Act 1988 (Cth) and Australian Privacy Principles
