How vehicle insurers can leverage AI and data analytics to determine accident liability

It can be extremely challenging to establish a profitable business model as a carrier in the car/vehicle/automotive insurance industry. Countless factors can come into play that affect your overall success as a business. One of the most intransigent issues is the difficulty in determining accident liability. Incomplete and unreliable information often feed into a game of he-said, she-said, while an often slow and clunky arbitration process can delay claims and negatively impact customer service.

However, with innovations in artificial intelligence and data analytics, there may finally be a way to streamline claims management and produce better outcomes for everyone involved.

Key liability challenges

Determining liability in an auto accident has always been a challenge, mainly for two key reasons:

1. Crash data is often incomplete.

As a first port of call, most motor insurers rely on gathering information from the Event Data Recorders of both vehicles to determine what was happening before a crash. These devices can provide much useful vehicle information such as speed, acceleration, braking and steering angle in the moments before and after a collision.

The problem is that accessing and interpreting the raw data from a vehicle’s EDR is neither simple nor intuitive. Insurers often need to rely on the use of a crash reconstruction specialist to make sense of the data, the cost of which can range from $2,000 to $5,000 per claim. The situation is made worse by the fact that there are many different types of EDRs, which can vary greatly in the data they gather from one system to another. What’s more, the nature of a crash can mean that certain data is not recorded, making EDRs an imperfect system even at the best of times.

Another source of intelligence that insurers use when investigating a claim is the First Notice of Loss submissions from the insured parties involved in the crash. These will typically include a brief description of the crash, the resulting damage, police reports and any photo or video evidence. However, biases and imperfect memory can skew the accuracy of these reports. Even crash reconstruction specialists can differ in opinion when it comes to interpreting more subjective information such as road conditions, weather and the activity of third-party vehicles moments before a collision.

2. The arbitration process can be unpredictable

Most insurers rely on arbitration instead of the courts when attempting to settle a disputed claim. But when there is incomplete or subjective information, even an arbitrator can struggle with piecing together a coherent narrative about a crash. Much of the information is submitted in a piecemeal fashion, making it difficult for either party to present a strong case or defend their position.

The inconsistent and unpredictable nature of arbitration often can leave insured parties unsatisfied with the arbitrator’s decision. Nevertheless, small claims usually end here as it rarely makes sense to take things to the courts, where legal costs can be in excess of the claim amount. When it comes to claims that end up in the courts, things can get complicated for everyone involved. Even if the court settles in favor of a client’s case, the client is unlikely to appreciate the ordeal they’ve been through.

Enter AI and data analytics

Technology is always advancing and when it comes to vehicle manufacturing, one of the biggest changes in the wind right now is the increasing use of AI software-equipped vehicles. Implanted within a vehicle’s Electronic Control Unit, AI software programs use raw signal data from existing vehicle sensors to track wheel speed, wheel angle, torque and gear position, and generate insights on road friction, surface conditions and vehicle health. These are factors that a more basic EDR system could not detect.

But for insurance carriers, the true value comes when AI software-equipped vehicles are also connected, so they can continuously record and store easy-to-read vehicle data on a cloud server. When drivers first purchase vehicles that come with AI software, they have the option of signing a consent form that allows the vehicle manufacturer to gather and store data on the vehicle. In return, the driver gets access to advanced insights. These advanced insights can provide them not only with helpful information on the vehicle’s health such as tire tread and engine performance but also help them get better and less expensive services such as roadside assistance, personalized insurance and more.

In the event of a collision, insurers can easily request crash data from the manufacturer. This data should provide insurers with a second-by-second picture of what the vehicle was doing in the lead-up to the collision. From this more accurate intel, liability can be more easily determined, leading to faster claims processing, easier arbitration and more satisfied clients.

Tapping into the data provided by a vehicle’s EDR after a collision has always been a challenge for insurers. Even when the data is there, it’s often too complex to decipher or fit within a larger context of how the collision occurred.

The advent of AI software-equipped connected vehicles could dramatically change the motor insurance landscape, allowing better claim outcomes for clients, shorter claims cycles and better operational efficiency that yields both cost savings and better customer service.

Shahar Bin-Nun is CEO of Tactile Mobility. He may be contacted at [email protected].

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