Deepfakes are a Threat to Insurers. Here’s How They Can Protect Themselves

Record high prices for vehicles (new and used), along with major advancements in AI-based technology have merged to create a new headache for the insurance industry: a growing number of claims based on deepfakes and shallowfakes. 

Fraudsters concoct phony claims using fake or doctored documents and images – either inflating their claims or even omitting damage to avoid their vehicles getting written off. The technology to manipulate those documents and images is so good that it makes the fakes nearly impossible to detect. 

The result? More payouts by the insurance company, and the subsequent passing on of those extra costs to hapless, honest consumers – who find themselves facing growing premiums.

Of course one fraud success begets another, creating a vicious cycle that, because of ever-improving technology, is very difficult to stop. But there is one proven way to break this cycle: AI-based vehicle incident documentation systems, like Ravin’s, which utilize thousands of data points to determine if the images and documents submitted to insurance companies are legit – or fake.

Fraud is easier and more worthwhile than ever

The financial incentive for phony claims has never been greater, given that the average price for a new vehicle is nearly an eye-watering $50,000, with attendant increases in repair costs. Meanwhile, manipulating images – by enhancing damage or even creating damage that wasn’t there in the first place – is easier than ever, thanks to ever-improving image and video manipulation technology. Professional fraud gangs could even concoct full-length videos, purportedly taken by vehicle dashcams, that detail accidents that never happened. 

Along with these deepfake images, fraudsters also use “shallowfake” documentation – using non-AI applications like Photoshop to manipulate images, or to change data on documents like Vehicle Identification Numbers , expired registrations, and insurance eligibility. And with more insurers accepting digital claims based on driver-supplied evidence, the opportunity for any sort of fraud is greater than ever. Fraud costs vehicle insurers – and their customers – billions of dollars a year. With premiums at an all-time high, insurers are desperate to reduce the number of phony claims they face.

A Robust Fraud-Proof System

Ravin’s technology offers a solution to this fraud for insurers. Unlike most other systems, Ravin’s system does not allow consumers to upload their own photos. Rather, its system, which can be operated on a mobile phone app, takes the images itself. In addition to capturing the images, the app picks up meta data such as inspection location, time, and other user behaviours making it virtually impossible to manipulate the content. In addition, Ravin can also cross check vehicle-specific information, thanks to partnerships with other data providers, such as those specializing in telematics and historical reports on vehicles, to detect potential fraud. The images are also compared with a large database of images of vehicles of the same type that have sustained similar damage. The AI-driven system analyzes the images to ensure that the damage makes sense – that the circumstances under which the accident occurred match the kind of damage that would be expected to result. 

In addition to actually detecting fraud, insurance companies using Ravin technology could prevent fraudsters from even trying to defraud them – by making clear to customers that their fraud detection is robust enough to detect scams, and that if caught, fraudsters will be prosecuted to the full extent of the law. That warning by itself might be enough to ward off at least some fraud attempts. 

With a significant reduction in fraud – and the associated payouts – insurance companies will be able to lower premiums for their honest customers. Customers will thus likely flock to insurers who pass on the savings to them – another benefit to users of this advanced AI-based system. With Ravin’s technology, insurers can protect themselves from bad customers – and acquire more good ones.