3 Types of Fraud Underwriting Automation Health Data Can Stop

The modernization of life insurance applications has created a structural vulnerability for chief underwriting officers. As carriers replace physical medical exams with digital workflows to enable instant-issue policies, bad actors have exploited the absence of in-person verification. Reinsurers and actuarial teams are now modeling a sharp rise in application manipulation, ranging from synthetic identities to sophisticated health misrepresentation. To close this gap, carriers are deploying underwriting automation health data pipelines that capture real-time physiological signals directly from an applicant's smartphone. By requiring live, active participation to extract biometrics, this technology forces a physical choke point that digital fraudsters cannot bypass.

"Deepfake identity attacks are now occurring globally every five minutes. In the insurance sector specifically, carriers experienced a 475 percent increase in synthetic voice and video fraud attacks in 2024 as criminals exploit automated application workflows."

• Entrust Research Team, Entrust (2024)

The role of underwriting automation health data in fraud prevention

When life insurers shifted toward accelerated pathways, the primary goal was reducing the time to issue. Forms were digitized, third-party data sources like prescription histories were integrated, and algorithmic scoring was deployed to triage risk. However, the initial iterations of these systems relied heavily on self-reporting and static documentation. A user typing answers into a web form provides no mathematical proof of their physical presence or actual physiological condition.

Underwriting automation health data changes the security architecture of the digital application. Instead of asking an applicant to upload a scanned PDF or answer a questionnaire about their blood pressure, advanced systems use remote photoplethysmography (rPPG) and optical sensors to measure blood flow, heart rate, and heart rate variability through a standard smartphone camera. Remote photoplethysmography measures the variations in light absorption in human skin caused by the cardiac cycle. As the heart beats, blood volume in the microvascular tissue of the face changes. A standard smartphone camera operating at standard frame rates can detect these microscopic color shifts, extracting a continuous pulse signal.

Because these physiological signals can only be extracted from a living human being with a beating heart, the data collection process serves a dual purpose. It gathers the required actuarial data to price the risk, and it acts as an absolute liveness check. A printed photograph, a silicone mask, or a digitally generated avatar cannot produce a valid, continuous pulse signal.

Fraud Vector	Traditional Digital Vulnerability	Underwriting Automation Health Data Defense
Identity Spoofing	Relies on static ID uploads which are easily manipulated by generative AI.	Requires real-time biometric capture with built-in liveness detection.
Material Misrepresentation	Depends on self-reported questionnaires for physical metrics and lifestyle habits.	Extracts objective physiological baselines directly from the applicant in real-time.
Proxy Applications	A healthy individual can fill out forms on behalf of an uninsurable applicant.	Face-matching technology confirms the person providing biometrics matches the ID continuously.

The implementation of these systems provides carriers with multiple distinct advantages over legacy digital checks:

• Eliminates reliance on static, easily forged documents and photographs.
• Verifies physical presence through active, continuous physiological measurement.
• Connects the stated identity directly to a living, breathing subject at the exact moment of application.
• Creates an auditable trail of biometric capture for reinsurer review and mortality studies.

Industry applications: 3 threats neutralized

1. stopping synthetic identity and deepfake spoofing

The most aggressive threat facing instant-issue life insurance is the synthetic identity. Criminals combine stolen social security numbers with fabricated names and AI-generated faces to create entirely new personas. Because these personas have no real-world medical history, they often pass through automated database checks undetected. According to industry research, synthetic identities account for the vast majority of identity fraud cases in the life insurance sector, costing the industry billions annually.

Underwriting automation health data immediately neutralizes synthetic identities. When an applicant is prompted to look into their camera for a health scan, the system requires an active response and functional physiological reactions. Generative AI video tools and deepfakes can simulate a face, but they cannot simulate the subtle changes in skin light absorption caused by actual capillary blood flow. The health data extraction process inherently functions as an unbreakable anti-spoofing mechanism. By tying the application to a physical cardiovascular system, carriers prevent synthetic profiles from entering the risk pool.

2. preventing material health misrepresentation

If synthetic identities are the most sophisticated threat, material misrepresentation is the most common. Applicants routinely understate their weight, deny tobacco use, and minimize chronic conditions when completing self-directed digital forms. Munich Re's 2024 U.S. Life Insurance Carrier Survey revealed that tobacco misrepresentation in accelerated programs reached an average of over 40 percent in 2023, while material misrepresentation related to cancer and infectious respiratory diseases also trended upward.

Relying on attending physician statements (APS) to catch these lies introduces weeks of delay, defeating the purpose of an accelerated program. By integrating underwriting automation health data into the initial application, carriers obtain an immediate, objective snapshot of the applicant's cardiovascular health. If a user claims to be a non-smoker with an elite athletic build, but their captured resting heart rate and heart rate variability align with significant cardiovascular stress, the automated system can flag the application for manual review. The technology replaces the honor system with observable, unalterable data.

3. deterring proxy application fraud

Proxy fraud occurs when an uninsurable or high-risk individual pays a healthy person to complete the medical portion of a life insurance application. In traditional paramedical exams, the examiner checks a physical driver's license before drawing blood. In a fully digitized questionnaire, that physical friction is gone. A broker attempting to push through a bad policy, or a family member acting on behalf of an uninsurable relative, can simply click the appropriate boxes and sign the electronic document.

In traditional digital applications that rely on typing or audio calls, proxy fraud is notoriously difficult to detect. Even if the carrier requests a photo ID, the person submitting the form can simply upload the true applicant's ID while answering the health questions themselves. Modern biometric underwriting platforms lock the session to the individual's face. The same camera that captures the physiological health data is simultaneously performing facial matching against the official identity document provided at the start of the session. If the face analyzing the heart rate does not match the driver's license on file, the session is terminated. This continuous identity verification ensures that the health data being underwritten belongs exclusively to the policyholder.

Current research and evidence

The quantitative case for deploying structural anti-fraud technology is supported by escalating loss ratios across the digital insurance sector. Research published by Entrust in 2024 indicated that deepfake incidents quadrupled between 2023 and 2024, representing a 40x cumulative growth rate over a two-year period. The financial services and insurance sectors are the primary targets, with average losses exceeding $600,000 per affected company due to advanced digital fraud.

Furthermore, actuaries from RGA and Munich Re have consistently warned that traditional fraud models are failing to catch AI-driven attacks. In a 2025 analysis by RGA researchers Colin M. DeForge and Jennifer Johnson, the authors noted that synthetic identity fraud operates as a technological arms race. Fraudsters are using machine learning to bypass static rules-based logic. As carriers deploy simple algorithms to catch anomalies, criminal syndicates deploy machine learning designed to generate application data that perfectly matches the carrier's ideal risk profile. The generated applicants have optimal physical ratios and pristine records. Without a physical biometric anchor, insurers are effectively fighting software with software, a battle that statistically favors the attacker.

By requiring the extraction of underwriting automation health data, carriers force the fraudster out of the digital realm and into the physical one. Advanced liveness detection reduces presentation attacks to near-zero percent, provided the detection relies on active, complex physiological markers rather than simple movement checks.

The future of anti-fraud underwriting

As instant-issue life insurance becomes the default consumer expectation, the security infrastructure supporting these programs must evolve from reactive to proactive. Chief underwriting officers can no longer afford to issue policies based solely on the assumption that a web form submission is authentic.

The next generation of actuarial science will treat the method of data capture as a primary risk variable. A policy priced on self-reported data will carry a higher risk premium than a policy priced on verified, real-time physiological metrics. As computer vision and remote photoplethysmography continue to advance, the depth of health data extracted via smartphone will increase, further solidifying the barrier against fraud. Carriers that adopt these technologies early will secure a significant competitive advantage, writing cleaner books of business while protecting their loss ratios from synthetic attacks and material misrepresentation.

Frequently asked questions

What is liveness detection insurance technology?

Liveness detection is a security protocol that verifies the source of a biometric sample is a live human being. In the context of life insurance, it prevents bad actors from using static photographs, pre-recorded videos, or AI-generated deepfakes to bypass identity verification during a digital application.

How does underwriting automation health data prevent misrepresentation?

By capturing actual physiological signals, such as resting heart rate and heart rate variability, the system generates an objective health baseline. This prevents applicants from falsifying their health status on digital questionnaires, as the physiological data is extracted passively and cannot be voluntarily manipulated.

Can synthetic identities bypass biometric health scans?

No. Synthetic identities are fabricated personas lacking physical bodies. Because biometric health scans require the optical measurement of real blood flow beneath human skin, a synthetic identity or digital avatar cannot generate the necessary physiological signals to complete the application process.

For chief underwriting officers and reinsurers building the next generation of instant-issue products, mitigating digital fraud requires securing the data collection process at the source. Circadify is actively addressing this space, providing carriers with the technology to extract verified physiological insights while stopping presentation attacks. To learn more about securing your accelerated pathways with robust biometric tools, explore our whitepapers and actuarial data at circadify.com/industries/payers-insurance.