Recently, The Guardian reported that some fraudsters are utilizing artificial intelligence photo editing software to manipulate photos for insurance fraud activities. This discovery has alarmed insurance companies as it could lead to a surge in car insurance premiums to unprecedented levels.

Allianz Insurance stated that incidents of tampering with real-life images, videos, and documents using applications have increased by 300% from 2021 to 2023. They stated, "This indicates that this could be one of the latest major scams facing the insurance industry."

Zurich UK Insurance also noted that they have seen a growing number of claims cases involving the use of artificial intelligence technology for tampering. A representative from the company stated, "From an anti-fraud perspective, this is becoming one of the emerging threats."

Although DEEPFAKE is typically created using artificial intelligence for prank images, videos, or documents, traditional editing software on smartphones and applications like Photoshop can also be used for forgery. Allianz stated that the rise of image editing and DEEPFAKE "poses significant risks to UK consumers."

Scott Clayton, Head of Claims Fraud at Zurich UK, stated that data forgery is very common in car insurance. Fraudsters use image editing software to add false license plates to deregistered vehicles.

He said, "We're finding more and more people finding total loss vehicles on broker websites and then implanting a registration onto that vehicle. They then make a claim against that vehicle, and claims handlers will value it at the surface value."

Clayton added that in the past, criminals intending to defraud insurance companies through "crash for cash" claims would need to have the vehicles collide with each other. "Now people can entirely create fraudulent claims behind a computer and profit from it, as these vehicles are already completely damaged."

The increase in fraudulent activities is one of the factors contributing to the rise in car insurance premiums. According to data from the Association of British Insurers, the average price of comprehensive car insurance in the UK rose by about one-third (33%) in the first quarter of this year compared to the same period last year, amounting to £157.

Insurance Companies: Enhancing Anti-Fraud Capabilities

With the advancement of technology, fraudsters are continually updating their methods, utilizing AI technologies including generative models, machine learning, and data analysis tools to deceive insurance companies. The application of AI technologies has become their new tool, making their criminal activities more covert and complex, challenging the anti-fraud measures of the insurance industry.

To effectively address this challenge, insurance companies need to continuously improve their anti-fraud technologies and utilize advanced data analysis and machine learning algorithms to identify and prevent potential fraudulent behaviors. At the same time, regulatory authorities and industry associations also need to strengthen regulation and cooperation in combating the increasingly serious issue of insurance fraud.

1. Establishing robust data risk control and analysis models to monitor abnormal patterns in insurance data and detect signs of fraudulent behavior promptly. Dingxiang Dinsight assists enterprises in risk assessment, anti-fraud analysis, and real-time monitoring, enhancing the efficiency and accuracy of risk control. Dinsight's average processing speed for daily risk control strategies is within 100 milliseconds, supporting configurable access and deposition of multi-source data. It can achieve self-performance monitoring and self-iterative mechanisms based on mature indicators, strategies, models' experience reserves, and deep learning technology. Xintell intelligent model platform, in conjunction with Dinsight, automatically optimizes security strategies for known risks and configures support for risk control strategies in different scenarios based on risk control logs and data mining of potential risks. It standardizes complex data processing, mining, and machine learning processes based on association networks and deep learning technology, providing one-stop modeling services from data processing, feature derivation, model construction to final model deployment.

2. Adopt identity verification technologies to ensure the authenticity of policyholders' identities while analyzing their behavioral patterns to identify anomalies. Dingxiang Device Fingerprinting generates unified and unique Device Fingerprinting for each device by internally connecting the information of multi-end devices. Analyzing whether there are abnormal behaviors such as multiple account logins, frequent IP address changes, and frequent device attribute changes that do not conform to user habits, it tracks and identifies fraudulent activities. Paired with Dingxiang's seamless verification based on AIGC technology, it prevents threats such as AI brute force attacks, automated attacks, and phishing attacks, effectively preventing unauthorized access, account theft, and malicious operations, thus protecting system stability.

3. Utilize image recognition technology to analyze photos, videos, and other materials in claim documents to detect possible forgery or tampering. Dingxiang's comprehensive panoramic face security threat perception solution intelligently verifies multidimensional information such as device environment, facial information, image authenticity, user behavior, and interaction status, quickly identifying more than 30 types of malicious attack behaviors such as injection attacks, live forging, image forgery, camera hijacking, debugging risks, memory tampering, Root/jailbreak, malicious ROM, simulator, etc. After timely discovering forged videos, false facial images, and abnormal interaction behaviors, it can automatically block operations. It can also flexibly configure the strength and friendliness of video verification, achieving dynamic mechanisms of seamless verification for normal users and enhanced verification for abnormal users.

4. Establish cooperation mechanisms with law enforcement agencies, other insurance companies, and third-party data providers to share information and intelligence, strengthening the monitoring and crackdown on fraudulent activities.

By adopting the above methods and building a secure system of multi-channel, all-scenario, and multi-stage protection, insurance companies can efficiently prevent various types of new fraud risks brought about by AI, safeguarding their own and their customers' interests.