AI-Driven Insurance Fraud: 2025 Trends and Countermeasures

Insurance fraud is entering a new era powered by artificial intelligence. Sophisticated fraud rings and lone scammers alike are exploiting generative AI tools to produce fake claims, synthetic identities, deepfaked evidence, and highly convincing impersonation scams. This whitepaper examines the latest 2025 trends in AI-driven insurance fraud – from AI-generated claims and documents to deepfake voice scams – and outlines how insurers can respond. We present recent data, real-world cases, and strategic insights for underwriters, fraud investigators, cybersecurity teams, claims managers, and C-suite leaders who need to understand this fast-evolving threat landscape.

The Scale of the Threat: Fraud enabled by AI is surging. A 2025 forensic accounting report found that AI-driven scams now account for over half of all digital financial fraud[1]. In insurance specifically, voice security firm Pindrop observed a 475% increase in synthetic voice fraud attacks at insurance companies in 2024, contributing to a 19% year-over-year rise in overall insurance fraud attempts[2]. Insurers face roughly 20 times higher fraud exposure than banks, due in part to heavy reliance on documents, images, and voice verifications in claims[3]. Figure 1 below illustrates the explosive growth of AI-enhanced insurance fraud incidents from 2022 to 2025, as multiple industry reports have indicated triple- or quadruple-digit percentage increases year-on-year in detected AI involvement in fraud.

Figure 1: Rapid rise of AI-enhanced insurance fraud cases (indexed growth 2022–2025). Industry data indicates exponential increases in AI-generated content found in fraudulent claims, especially since 2023.[4][2]

AI-Generated Claims and Fake Evidence

One of the most prevalent trends is the use of generative AI to craft entirely fabricated insurance claims. With advanced AI text generators, fraudsters can write realistic incident descriptions, medical reports, or police statements at the click of a button. These AI-written narratives often read as polished and plausible, making it harder for adjusters to spot inconsistencies. For example, fraudsters have used ChatGPT to draft detailed accident descriptions or injury reports that sound professional and convincing – a task that once required significant effort and writing skill.

More concerning is that criminals now pair these fake narratives with AI-generated supporting evidence. Image generation models (like Midjourney or DALL-E) and editing tools can produce photorealistic damage and injury photos. According to industry reports, some drivers have begun submitting AI-generated images to exaggerate vehicle damage in auto claims[5]. Generative AI can create a photo of a wrecked car or flooded home that never actually existed. These images are often more realistic than what older Photoshop techniques could achieve[6], making them difficult to detect with the naked eye. In April 2025, Zurich Insurance noted a rise in claims with doctored invoices, fabricated repair estimates, and digitally altered photos, including cases where vehicle registration numbers were AI-inserted onto images of salvaged cars[7][8]. Such fake evidence, when combined with a well-crafted AI-written claim form, can slip past manual reviews.

A striking case in the UK involved fraudsters taking a social media photo of a tradesman’s van and using AI to add a cracked bumper, then submitting it along with a fake repair invoice for £1,000 as part of a false crash claim[9]. The scam was only uncovered when investigators noticed the same van photo (pre-damage) on the owner’s Facebook page[10]. This illustrates a broader phenomenon: insurers report a 300% jump in “shallowfake” image edits (simple digital manipulations to add damage or alter details) in just one year (2021–2022 vs 2022–2023)[4]. Allianz UK warned in 2024 that digital photo distortions and fake documents had “all the signs of becoming the latest big scam to hit the industry”[4]. Zurich’s head of claims fraud likewise observed that what used to require staging a physical car crash can now be done entirely behind a computer – scammers can “create a fraudulent claim from behind their keyboard and extract significant sums” with fake total-loss photos and reports[11][12]. This shift not only increases the volume of fake claims but also lowers the barrier to entry for would-be fraudsters.

Never Worry About AI Fraud Again. TruthScan Can Help You:

Detect AI generated images, text, voice, and video.
Avoid major AI driven fraud.
Protect your most sensitive enterprise assets.

Try for FREE

Beyond autos, property and casualty claims are seeing AI-assisted inflation of losses. There are reports of fake photos for travel insurance (e.g. luggage “damage” or staged theft scenes) and AI-generated receipts for expensive items that were never purchased. Life and health claims are not immune either – fraudsters have generated bogus medical bills and death certificates using AI document forgers. In fact, Zurich noted deepfake technology being used to create entirely fictitious engineer assessments and medical reports in claims packages[11]. These AI-generated documents, often complete with realistic logos and signatures, can be indistinguishable from genuine paperwork. An emerging concern for life insurers is obituary and death certificate fraud: criminals can use AI to produce fake obituaries or doctor’s letters to support a death claim for someone who is actually still alive (or who never existed at all, as discussed next).

Synthetic Policyholders and Identities

Perhaps the most insidious development is synthetic identity fraud in insurance. Synthetic identity fraud involves creating a fictitious person or entity by combining real data (stolen Social Security numbers, addresses, etc.) with fabricated details (fake names, fake ID documents). Advances in AI have made it trivially easy to generate realistic personal profiles – including photos and IDs – for people who don’t exist[13][14]. Fraudsters can now algorithmically produce a completely fake customer, purchase a policy in their name, and later stage claims or policy benefits for that fake identity.

In the life insurance sector, synthetic identity schemes have skyrocketed. Industry research in 2025 estimates synthetic identity fraud costs over $30 billion annually, accounting for as much as 80–85% of all identity fraud cases across financial services[15][16]. Life insurers have been hit particularly hard: crooks have been known to secure life insurance policies for a fictitious person and then “kill off” that person on paper to collect the death benefit[17]. For example, a fraudster might create a synthetic client “John Doe,” pay premiums for a year, then submit a claim with a fake death certificate and obituary for John Doe’s untimely demise. Because the identity was built carefully (credit history, public records, etc.), the death claim can appear legitimate – until no actual body or real relatives can be found. By the time the fraud is discovered, the perpetrators are long gone with the payout.

Synthetic identity schemes also plague health insurance and auto insurance. Criminal rings create “Frankenstein” identities by using children’s or elderly persons’ Social Security numbers (which have no credit history) combined with AI-generated names and driver’s licenses[15]. They then buy health policies or auto policies for these fake individuals, and shortly thereafter file large claims. One variant is syndicates setting up fake businesses (shell companies) – for instance, a sham trucking company – and purchasing commercial insurance for it, only to later claim on staged accidents or phantom employees’ injuries[18][19]. Because the business exists only on paper (with AI-generated business registrations and tax IDs), this “entity-based” synthetic fraud often isn’t uncovered until after claims are paid out[18][20].

Why are synthetic identities so effective? For one, they often sail through automated identity verification checks. Credit bureaus and KYC systems may find no red flags because the identity includes some real, valid data (e.g. a real SSN with a clean record)[21]. Meanwhile, AI-generated profile photos and ID scans can look completely authentic – today’s AI can produce a human face that even advanced facial recognition might accept as real. The result: most automated systems recognize these profiles as legitimate and the fraud is only caught (if at all) after major losses[22].

Real-world impact: RGA reports that synthetic identity fraud in life insurance now costs the industry about $30B per year and has grown almost 400% since 2020[15][16]. The U.S. Federal Trade Commission estimates synthetic IDs comprise the vast majority of identity fraud incidents[16]. These losses ultimately hit honest policyholders’ wallets – every family pays an estimated \$700 extra in premiums each year due to the broader fraud load on insurers[15]. Insurers are responding by beefing up verification at onboarding and claims: implementing database checks for identity linkage, monitoring for multiple policies at the same address, and even running “liveness” tests (selfie video checks to ensure a claimant is a real person, not just an AI image)[23][24]. But as we’ll see, fraudsters are countering with AI in the next arena: deepfake voices and videos.

Deepfake Voices and Video Claims

AI-generated audio and video deepfakes add a alarming new dimension to insurance fraud. In 2023 and 2024, there were several incidents of criminals using voice cloning to impersonate individuals over the phone – a tactic originally seen in bank heists (like the infamous deepfake CEO phone call that stole $35 million in 2020) but now spreading to insurance. Fraudsters are cloning the voices of policyholders, physicians, or claims adjusters and using them in social engineering scams. Pindrop’s 2024 analysis warned that “deepfakes, synthetic voice tech and AI-driven scams are reshaping the fraud landscape”, with voice fraud scaling at an unprecedented rate[25]. They found insurance call centers were bombarded by overseas bad actors using voice deepfakes: for instance, calls would come in providing a real policyholder’s stolen SSN and personal data, and if an agent answered, the caller’s AI-cloned voice could trick the agent through knowledge-based authentication and request a fraudulent disbursement[26]. In one West Coast insurer’s case, attackers repeatedly used this method to try to take over accounts and redirect payouts, exploiting the fact that call center ID verification was relying on voice and personal info that can be spoofed[26].

Voice impersonation has also been used on the consumer side: Scammers have called accident victims or beneficiaries while pretending to be insurance representatives, using AI voices to sound official, in order to phish sensitive information or even payments. Conversely, a fraudster might impersonate a customer on a claims hotline to file a claim via phone using a deepfake voice that matches the customer’s gender/age, thus bypassing voice-biometric checks. Recent fraud statistics are sobering: industry experts project a 162% growth in deepfake fraud attacks against insurers in the coming year[27], and Pindrop recorded a 475% spike in synthetic voice attacks in 2024 as mentioned earlier[2]. These attacks are quickly outpacing more traditional cyber fraud vectors.

Beyond phone calls, video-based deepfakes are emerging in the claims process. Many insurers adopted virtual claims inspections and video conferencing (accelerated by the pandemic), to verify losses or interview claimants remotely. Now, fraudsters are leveraging AI avatars and deepfake videos to fool these verifications. There have been reports of claimants using AI-generated avatars on live video calls with adjusters, in order to masquerade as someone else or to conceal signs of inconsistency[28]. For example, a fraud ring might use a deepfake “live” video of a supposed claimant showing their damage via smartphone, when in fact the person on camera is an AI-synthesized composite or a hired actor wearing face-altering filters. One speculative but plausible scenario is using a deepfake of a deceased person: In an annuity or life insurance fraud, a family member could employ a deepfake video of the recently deceased during a routine proof-of-life call to continue receiving payouts[29]. While no high-profile case of this nature has been publicized yet, insurers are bracing for it. Regulators are also taking note – discussions are underway in the U.S. and Europe about classifying deepfakes under identity theft and updating guidelines for evidence verification in insurance[30].

Detecting deepfake videos and audio is very challenging without technical tools. Human adjusters aren’t trained to discern subtle lip-sync issues or acoustic oddities. However, there have been red flags in some instances: for example, unnatural blinking or facial glitches on video, or background audio artifacts on a call that tipped off investigators. Overall though, deepfake insurance fraud is still in early stages, and prosecution remains rare – as of 2023, legal definitions were unclear and proving a video was AI-generated was difficult without expert analysis[31]. This gives fraudsters a sense of impunity. The arms race is on: insurers are now turning to forensic AI to fight AI, deploying deepfake detection algorithms to scrutinize suspicious videos frame-by-frame for signs of manipulation[24]. Voice biometrics vendors are rolling out deepfake voice detectors that analyze spectral patterns and vocal cadence for authenticity[32]. We will discuss these defensive technologies in a later section.

AI-Enhanced Phishing and Impersonation Scams

Not all AI-enabled fraud comes through the claims department; some targets are customers and employees via social engineering. Phishing emails and texts crafted by AI have become a major threat in the insurance domain. In these schemes, fraudsters use AI chatbots and translation tools to generate highly convincing scam communications. For example, criminals can impersonate an insurance company’s branding and writing style to send out mass phishing emails to policyholders, telling them “urgent action is needed to prevent policy cancellation” and directing them to a fake website. Unlike the clumsy scam emails of the past, AI ensures impeccable grammar and even personalization, making them far more believable. We’ve seen AI used to scrape social media for details that get woven into spear-phishing messages, such as referencing a recent car purchase in a fake auto insurance notice.

Another vector is AI-aided impersonation of agents or executives. There have been cases where scammers cloned the voice of an insurance agency owner and left voicemail messages for customers asking for bank info updates – effectively a voice phishing attack. Similarly, internal fraud can stem from AI impersonation: one insurer’s finance department nearly fell victim when fraudsters sent a deepfake audio message purportedly from the CEO authorizing a fund transfer (a variant of “CEO fraud” now covered under some e-crime insurance policies[33]). These kinds of AI-driven impersonation scams rose 17% in 2023 according to Liberty Specialty Markets[33], and are expected to keep rising.

Consumers are also being targeted with synthetic media scams tied to insurance. The Coalition Against Insurance Fraud notes instances of impostors posing as insurance adjusters, contacting accident victims claiming they are handling their claim and then demanding immediate payment or sensitive data[23]. Unsuspecting customers, relieved to hear from a supposed representative, may comply especially if the caller knew details of their accident (which AI could glean from hacking or public sources). Public awareness of these tactics is low; hence fraud prevention experts urge insurers to educate policyholders about verifying identities of callers and emails[23][34]. Just as banks warn customers about phishing, insurers in 2025 are starting to include deepfake scam warnings in their communication.

One common thread in these AI-enhanced schemes is the use of readily available “fraud-as-a-service” kits[35]. On the dark web, criminals can buy or subscribe to tools that provide pre-made deepfake voices, fake document templates, phishing email generators, and more. This democratization of AI tools means even low-skilled scammers can launch sophisticated fraud attacks[35]. For insurance companies, this translates to a deluge of more convincing fraud attempts coming from all angles – claims, customer service, email, even social media. It underscores the need for a multi-pronged defense strategy, combining technology, human vigilance, and cross-industry collaboration.

Detection and Defense: An AI-Powered Response

Fighting AI-driven fraud requires AI-driven defense. Insurers are increasingly turning to advanced detection technologies and revamped processes to counter the onslaught. In essence, insurers must embed content authentication checkpoints throughout the insurance lifecycle – from underwriting to claims to customer interactions – to sniff out AI forgeries. Figure 2 provides a breakdown of key fraud types enabled by AI and their prevalence, and the following sections detail how to detect and deter each.

Figure 2: Types of AI-enhanced insurance fraud in 2025 (estimated share by scheme). Fake imagery (doctored photos of damage) and synthetic identities are among the largest categories, followed by AI-forged documents (e.g. receipts, certificates) and deepfake audio/video scams.

1. AI Content Detection Tools: New AI detection services can analyze text, images, audio, and video to determine if they were machine-generated or manipulated. For example, insurers can leverage solutions like TruthScan’s AI Text & Image Detectors which use 99%+ accurate AI to flag AI-written documents or doctored photos[36]. An insurance company could integrate these detectors into their claims system: when a claim and its evidence are submitted, the text description can be automatically scanned for AI-generated language patterns, and any uploaded images can be scanned for telltale signs of CGI or editing. Enterprise-grade tools can identify AI-generated text in documents, emails, and communications with 99% accuracy[36], and similarly detect AI-generated or manipulated images to ensure visual content authenticity[36]. This means a fake accident narrative produced by ChatGPT or a midjourney-faked damage photo would be flagged for manual review before the claim gets processed. Insurers in 2025 are increasingly adopting such AI content authentication – in fact, 83% of anti-fraud professionals plan to integrate generative AI detection by 2025, according to an ACFE survey, up from just 18% using it today[37][38].

2. Identity Verification & Biometric Checks: To tackle synthetic identities, insurers are enhancing KYC protocols with AI as well. Identity validation services can cross-verify applicant data against multiple databases and use facial recognition with liveness tests. For example, requiring a short video selfie during onboarding (and using face matching to the ID provided) can thwart many synthetic IDs. Even more high-tech, companies like TruthScan offer image forensics that can spot AI-generated profile photos, avatars, and synthetic persona images – their AI image detector is trained to identify faces made by generators like StyleGAN or ThisPersonDoesNotExist[39]. By deploying such tools, an insurer can detect if a life insurance applicant’s selfie is not a real human. On the voice side, voice biometric authentication for customer service calls can help; modern voice AI detectors are able to identify synthetic voices and voice cloning attempts in real time[40]. For instance, TruthScan’s AI Voice Detection system uses acoustic analysis to recognize AI-generated voices and audio deepfakes before they fool call center staff[40]. These solutions act like a firewall – if someone calls pretending to be John Doe and their voiceprint doesn’t match John Doe’s authentic voice (or matches known deepfake characteristics), the call can be flagged or further identity proof required. Multi-factor authentication (email/SMS confirmation, one-time passcodes, etc.) adds additional hurdles for impersonators to overcome.

3. Deepfake Video & Image Forensics: When it comes to video evidence, insurers are starting to implement specialized forensic analysis. Advanced software can analyze video metadata, frame consistency, and error levels to detect deepfakes. Some tools examine reflections, shadows, and physiological cues (like pulse in a person’s throat on video) to ensure a video is genuine. Metadata forensics is also valuable: examining file metadata and generation footprints in images or PDFs can reveal if something was likely produced by an AI tool[41]. Insurers should require original photo files (which contain metadata) rather than just screenshots or printed copies, for instance. Zurich’s fraud team noted success in catching fake car images by noticing anomalies in the image metadata and error level analysis[41]. Email scam detectors can likewise scan inbound communications for signs of AI-written phishing content or known malicious signatures[42]. Many insurers now run phishing simulations and AI-drafted scam examples in employee training to raise awareness.

4. Process Changes and Human Training: Technology alone isn’t a silver bullet. Process enhancements are being made, such as more frequent random in-person spot-checks for high-value claims, or requiring physical documentation in certain cases. Some insurers have delayed fully automated straight-through processing for claims, re-inserting human review for claims that score high on an AI-fraud risk model. On the human side, training is crucial. Fraud investigators and adjusters are being trained to recognize AI red flags: e.g., multiple claims using identical wording (ChatGPT “style”), images lacking true randomness (e.g., repeating patterns in what should be organic damage), or voices that sound almost right but have robotic cadence. Insurers are also educating customers: sending out fraud alerts about deepfake schemes and advising how to verify an insurance representative’s identity (for instance, providing a known callback number).

5. Collaborative Efforts: Industry-wide collaboration is ramping up. In the UK, the Insurance Fraud Bureau and Association of British Insurers have formed working groups on AI fraud, and the government’s Insurance Fraud Charter (2024) is fostering data sharing and joint initiatives[43]. Globally, insurers are partnering with cybersecurity firms and AI startups. Notably, new insurance products are appearing: Liberty Mutual launched an e-crime insurance for SMEs that specifically covers deepfake scams and CEO fraud[44][33], highlighting that this risk is very real. This also means insurers might find themselves both victims and solvers of AI fraud – paying out on a deepfake scam if not detected, but also offering coverage for others who suffer such attacks.

The integration of detection technology into workflows can be visualized as a multi-point defense in the claims lifecycle. As shown in Figure 3, insurers can insert AI verification steps at policy application (to screen for synthetic identities via ID document and selfie checks), at claim submission (to automatically analyze uploaded documents, photos, or audio for AI-generation), during claim review/investigation (to perform deepfake forensic analysis on suspicious evidence and verify any voice interactions), and right before payout (a final identity authentication to ensure the payee is legitimate). By catching fraud early – ideally at onboarding or first notice of loss – insurers save investigative costs and avoid wrongful payouts.

Figure 3: Integration of AI detection points in the insurance lifecycle. At policy onboarding, AI-based identity validation checks for synthetic or fake identities. When a claim is submitted, automated detectors scan claim text, documents, and images for AI-generated content. During claims review, specialized deepfake and voice analysis tools verify any audio/video evidence. Prior to payout, biometric identity verification confirms the beneficiary’s identity. This multi-layered approach helps intercept fraud at multiple stages.

Insurers don’t have to build all these capabilities in-house – many are turning to enterprise solutions like TruthScan’s AI Detection Suite, which offers a range of tools that can be API-integrated into insurer systems. For example, TruthScan’s AI Image & Deepfake Detection service can be used to verify the authenticity of images and videos with over 99% accuracy[45]. Their AI Text Detector flags AI-written text in claims or emails[36], while the AI Voice Detector provides voice cloning detection and speaker verification to stop phone imposters[40]. There are even niche tools like a Fake Receipt Detector to instantly analyze invoices/receipts for signs of tampering or AI-generated fonts/layouts[46] – extremely useful given the prevalence of fake repair bills in claims. By deploying a combination of these tools, an insurer can drastically improve their fraud detection rate. One Fortune 500 insurer reported catching 97% of deepfake attempts in 2024 by using a layered AI screening approach (text, image, voice) and thereby avoiding an estimated \$20M in losses[47][48].

Conclusion

AI is transforming the insurance fraud battlefield on a global scale. Fraudsters are innovating with generative AI to create falsehoods that are more convincing than ever – from wholly fabricated people and accidents to impersonations that can deceive even seasoned professionals. The data from 2024–2025 shows an alarming growth in these AI-fueled schemes, but it also highlights that insurers who invest in detection and prevention can stay a step ahead. By combining cutting-edge AI detection technology – like image forensics, voice authentication, and text analysis – with updated workflows and education, the industry can mitigate the risks without sacrificing the efficiencies that digital processes bring.

At its core, this is a technological arms race[49]. As one fraud prevention expert noted, “In this new reality, vigilance is the premium that must be paid”[50]. Insurance companies must foster a culture of vigilance and leverage the best tools available to preserve trust in the claims process. That means verifying the truth of documents, voices, and images with the same rigor that underwriters use to assess risk. It also means collaborating across the industry to share intelligence on emerging AI fraud tactics and jointly develop standards (for example, standard metadata requirements for submitted media, or industry blacklists of known fake identities).

2025 is a tipping point: the insurers who proactively adapt to AI-driven fraud will protect their customers and balance sheets, while those slow to respond may find themselves targets of headline-grabbing scams. The encouraging news is that the technology to fight back exists and is rapidly maturing – the same AI that empowers fraudsters can empower insurers. By implementing solutions such as TruthScan’s multi-modal AI detection suite for claims and identity verification, insurers can dramatically reduce the success rate of AI-generated fraud attempts[51][52]. In doing so, they not only prevent losses but also send a clear message to would-be fraudsters: no matter how clever the tools, fraud will be uncovered.

In summary, AI-driven insurance fraud is a formidable challenge but one that can be met with an equally intelligent defense. With vigilance, cross-functional strategy, and the right technology partners, the insurance industry can continue to uphold the fundamental promise at the heart of its business – to pay only legitimate claims, and to do so swiftly and securely in an increasingly digital world.

References:

Association of British Insurers – Fraud statistics 2023[53][54]
Allianz & Zurich on AI-“shallowfake” claim surge – The Guardian, 2024[4][11]
Facia.ai – “Deepfake Insurance Fraud: How AI is Rewriting the Rules,” Oct 2025[55][56]
Coalition Against Insurance Fraud – Synthetic Fraud in Insurance (Quantexa), Dec 2024[21][17]
RGA – “Fraud Fight’s New Frontier: Synthetic Identities,” June 2025[15][16]
Pindrop – Voice Fraud Report, via FierceHealthcare, Jun 2025[2][3]
Turning Numbers – “Top Financial Fraud Schemes 2025,” Oct 2025[1][57]
TruthScan – AI Detection Platform (services overview), 2025[51][52]
TruthScan – AI Image Detector product page, 2025[45][39]
TruthScan – AI Voice Detector product page, 2025[40]
TruthScan – Fake Receipt Detector product page, 2025[46]
Liberty Specialty Markets – Deepfake/Cyber Fraud Insurance Press Release, Mar 2025[33]
Facia.ai – Blog: Insurance Fraud Prevention Arms Race, 2025[24][32]
Insurance Business UK – “AI-generated images used for motor fraud,” Apr 2025[7][58]
The Guardian – “Fake car damage photos alarm insurers,” May 2024[9][12]

[1] [35] [57] 2025 Financial Fraud Schemes: AI Threats and Red Flags

https://www.turningnumbers.com/blog/top-financial-fraud-schemes-of-2025

[2] [3] [25] [26] Insurance fraud increased by 19% from voice attacks in 2024

https://www.fiercehealthcare.com/payers/insurance-fraud-increased-19-synthetic-voice-attacks-2024

[4] [9] [10] [11] [12] Fraudsters editing vehicle photos to add fake damage in UK insurance scam | Insurance industry | The Guardian

https://www.theguardian.com/business/article/2024/may/02/car-insurance-scam-fake-damaged-added-photos-manipulated

[5] [6] [7] [8] [43] [53] [54] [58] AI-generated images now being used for motor insurance fraud – report | Insurance Business UK

https://www.insurancebusinessmag.com/uk/news/technology/aigenerated-images-now-being-used-for-motor-insurance-fraud–report-532346.aspx

[13] [14] [15] [16] Fraud Fight’s New Frontier: Synthetic identities and an AI arms race | RGA

https://www.rgare.com/knowledge-center/article/the-fraud-fight-s-new-frontier–synthetic-identities-and-an-ai-arms-race

[17] [18] [19] [20] [21] [22] JIFA: Synthetic Fraud: With Synthetic Fraud Already in Their Ecosystem, Insurers Need to Think More Like Banks – InsuranceFraud.org

https://insurancefraud.org/publications/jifa-synthetic-fraud/

[23] [24] [28] [29] [30] [31] [32] [34] [41] [49] [50] [55] [56] Deepfake Insurance Fraud: How AI Is Rewriting the Rules of Insurance Claims