Insurance fraud is a growing problem that costs the industry billions of dollars, and we’re all paying the price. We’ve heard it repeatedly; fraud is widespread, expensive, and ultimately impacts every policyholder. Insurance companies have a responsibility and even a legal obligation to protect their clients from the financial consequences of fraudulent activity.¹ But what does that actually mean for the claims examiner on the front lines?

The following examines the issue of medical fraud in the liability arena and identifies a few of the key tools and processes claims examiners can use to help combat it.

Medical Records Review

Often the settlement package from a plaintiff representative simply seems off. For example, perhaps there was no treatment at the scene of the accident, yet the file includes records showing months, or sometimes years of treatment, along with extensive diagnostic tests or procedures. The file may show surgery, maybe several surgeries, and a claim that the plaintiff will never work again, accompanied by a significant lost-wage claim. While the red flags may not jump off the page, they are often buried somewhere in the documentation.

Is there a pre-existing condition that was not disclosed? Were medical records falsified, or invoices provided for services not rendered? You may encounter double billing, phantom billing and upcoding, which is billing for a more expensive service than the one actually provided.

Claims examiners must be diligent and detail oriented in their review to identify these red flags and recognize potential fraud.

Fraud Detection

There are several things a claims examiner can do to identify potential fraud.

Engage a neutral physician to conduct an Independent Medical Exam (IME) to help validate the extent and cause of injuries. Avoid over-reliance on the same examiner, which can undermine credibility.

Conduct an audit of medical bills to ensure the treatment provided matches the type and severity of loss claimed. An audit can also uncover inflated charges or procedures that don’t align with the reported injuries.

Monitor social media for inconsistencies between a claimant’s reported limitations and their actual activity level. Consider surveillance to further confirm or dispute any discrepancies.

Use artificial intelligence (AI) applications such as pattern recognition, predictive analytics and natural language processing (NLP) to quickly identify inconsistencies.² The insurance industry is experiencing a fundamental shift as advanced analytics and AI accelerate the detection of suspicious activity, allowing potential fraud to be flagged earlier and more accurately than ever before.³

The Double-Edged Sword of AI

While AI has the potential to transform the industry, it has also handed sophisticated tools to a new generation of bad actors. The emergence of generative AI tools has fundamentally lowered the barrier to committing insurance fraud.

Industry executives anticipate using AI to gain an advantage and improve rates and customer experience. There is pressure to implement AI to keep up with rivals. However, there is concern of the risks that come along with AI including inaccuracy and bias. So, as AI is implemented, here are some things to look out for in the day-to-day process of claims handling.⁴

According to the National Insurance Crime Bureau (NICB), the availability and ease of use of these technologies have made fraud enticing to individuals who may not have otherwise considered filing a false claim.⁵ What once required a network of corrupt medical providers, forged signatures, and physical document manipulation can now be accomplished in minutes using widely available AI tools.

Criminals can use AI tools to generate up to 20,000 fake IDs at a time and sell them online for as little as $5 each.⁶ They use generative AI to create, alter, or forge police accident reports and medical records to support fraudulent insurance claims. In addition, AI‑powered tools can clone or synthesize voices to create a convincing persona capable of sending voice messages or handling live phone calls with insurance personnel.

NICB has identified a range of indicators that may suggest submitted claim materials are AI‑generated or artificially altered. Claims examiners should familiarize themselves with the common warning signs to better recognize when a submission may require deeper scrutiny.

Photos and Videos

Submitted visual evidence may be AI‑generated or manipulated if it contains:

• Abnormal Lighting – Inconsistencies between the apparent light source and the shadows or reflections visible in the image
• Body Part Oddities – Unusual depictions of hands, eyes, or ears; inconsistent skin tone (particularly at the neck or wrists); irregular hair or teeth patterns
• Incoherent Background Elements – Signage that does not make sense; objects that appear out of place or physically inconsistent with their surroundings
• Sound Irregularities – Unnatural or robotic speech patterns, inconsistent pitch, awkward pauses, unusual breathing sounds, or background noise that doesn't match the claimed environment
• Video Irregularities – Odd cuts, distorted or shifting backgrounds, unnatural facial expressions, or movements that appear uncanny or distorted
• Metadata Inconsistencies – Compression artifacts that are incongruous with the stated file format, or inconsistent compression artifacts across an image or its color channels

Written Documents and Text

Submitted written materials – including medical records, police reports, and claim narratives – may be AI‑generated if they exhibit:

• Unusual Writing Patterns – Repetitive language, overuse of buzzwords or common phrases, overly complex sentence structures, or the presence of placeholder text such as “[insert name]”
• Absence of Natural Language – A notable lack of informal language, colloquialisms, or slang that would typically appear in organic human communication
• Excessive Use of AI‑associated Vocabulary – Words and phrases that AI models commonly overuse, including crucial, delve, dive, tapestry, furthermore, consequently, and not only but
• Identical Supporting Documentation Across Multiple Claims – Particularly written evidence such as police reports that use suspiciously similar standardized language across different claimants or incidents

Additional Red Flags

• Results from online third-party AI detection tools flagging submitted text, images, audio, or video as potentially AI‑generated
• Multiple claims sharing identical or near-identical supporting documentation, which may indicate a coordinated fraud scheme

Once you have determined that a claim may involve fraud, the next step is to report your suspicions. This may involve notifying your internal SIU department or submitting the case to an approved external vendor used by your company.

Additionally, NICB members can refer questionable claims to NICB for potential investigation. Detailed investigative leads referred to NICB are reviewed by field operations team members for actionability and included in aggregate data for trend analysis and intelligence reports.

While fraud may seem difficult to prove, intentional fraud can often be inferred from the “totality of the circumstances,”⁷ even if the scheme was unsuccessful.⁸

Looking Ahead

The intersection of generative AI and insurance fraud is not a future problem – it is a current and rapidly escalating one. As AI tools become more powerful, more accessible, and more affordable, the volume and sophistication of fraudulent claims are likely to increase.

Claims examiners and insurance professionals who remain vigilant, informed, and equipped with the right tools will play a critical role in protecting the integrity of the insurance system – ultimately protecting consumers from bearing the cost of fraud through higher premiums and reduced coverage access.