A new wave of vehicle insurance fraud fueled by generative AI

View on arXiv ← Back to list

Authors: Amir Hever, Itai Orr

Published: 2025-10-22 18:31:31+00:00

AI Summary

Generative AI is accelerating vehicle insurance fraud by enabling the mass creation of realistic fake damage photos and videos, often evading current AI forensic detection methods. This paper proposes the UVeye layered security solution, which utilizes automated, physical vehicle scans and encrypted digital fingerprinting to provide objective, tamper-proof evidence. This system ensures claims are verified against the physical reality of the vehicle, reducing reliance on potentially manipulated claimant-submitted media.

Abstract

Generative AI is supercharging insurance fraud by making it easier to falsify accident evidence at scale and in rapid time. Insurance fraud is a pervasive and costly problem, amounting to tens of billions of dollars in losses each year. In the vehicle insurance sector, fraud schemes have traditionally involved staged accidents, exaggerated damage, or forged documents. The rise of generative AI, including deepfake image and video generation, has introduced new methods for committing fraud at scale. Fraudsters can now fabricate highly realistic crash photos, damage evidence, and even fake identities or documents with minimal effort, exploiting AI tools to bolster false insurance claims. Insurers have begun deploying countermeasures such as AI-based deepfake detection software and enhanced verification processes to detect and mitigate these AI-driven scams. However, current mitigation strategies face significant limitations. Detection tools can suffer from false positives and negatives, and sophisticated fraudsters continuously adapt their tactics to evade automated checks. This cat-and-mouse arms race between generative AI and detection technology, combined with resource and cost barriers for insurers, means that combating AI-enabled insurance fraud remains an ongoing challenge. In this white paper, we present UVeye layered solution for vehicle fraud, representing a major leap forward in the ability to detect, mitigate and deter this new wave of fraud.

Key findings

Generative AI tools can easily fabricate convincing fraudulent claims, highlighting the limitations of existing detection methods like metadata checks and image forensics. The UVeye pilot program demonstrated significant operational improvements, including shortened cycle times, enhanced damage assessment accuracy (especially undercarriage damage), and improved identification of pre-existing damage, proving physical scanning is a strong fraud deterrent.

Approach

The UVeye solution uses a three-layer defense: trusted third-party oversight, automated scanning using multiple cameras/frames to capture vehicle condition (including the undercarriage), and embedding an encrypted digital fingerprint in the scan data. An AI system analyzes the scan results to assess damage severity and compares this assessment to the policyholder's submitted claim information, flagging discrepancies as potential fraud.

Datasets

UNKNOWN

Model(s)

UNKNOWN

Author countries

UNKNOWN

← Previous