A commercial automotive insurance provider offering a variety of coverages for cars, trucks, vans, and other vehicles used for business.
Financial Services & Insurance
- Delayed insight & complex risk driver identification process
- Expensive data engineering overhead
- High cost for risk misalignment
- Faster, more efficient claims processing
- Lower overhead and easier claims litigation process
- Proactive, real-time FNOL notification
A commercial automotive insurance provider was looking to increase the efficiency of its claims-handling process. They wanted to use AI to identify claims events and more efficiently gather all the telematics and video footage for each claim to reduce the total processing time.
The insurance provider collects telematics data daily across their entire fleet of insured vehicles. They also maintain data on claims filed by policyholders. When a claim is filed, the insurer must sift through telematics data and video feeds to find information about that specific incident and relate it to the claim.
However, claims are often not filed immediately when an accident occurs, resulting in human error and potential day/time/location inaccuracies—making it difficult and time-consuming for the agents to find the correct details, in some cases before certain data is overwritten. This inefficient process results in longer claims processing times and more difficult litigation.
By deploying Devron’s federated data science platform across the insurer’s claims and third-party telematics databases, Devron experts were able to tag telematics events and build a supervised learning time-series model that accurately detects accidents. This provided a narrow window of time against which agents could more efficiently review video footage to confirm the incident and associate it with a specific claim.
In addition, Devron experts identified unique accident signatures, allowing the insurer to instantly detect accidents when they occur based on telematics information, accelerating their first notice of loss to be virtually real-time with the incident itself. As a result, they could proactively file claims and contact drivers to provide immediate assistance.
Devron enabled the insurer to gain new agility and efficiency in their claims processing operations—accelerating timelines, reducing costs, and improving customer experience.
Devron enables data science teams to realize enhanced outcomes, improved data science agility, and reduced overhead—all without moving or exposing data.
Faster Claims Processing
The insurer identified accidents faster—in many cases before a claim was filed. This enabled them to gather the necessary video footage quickly and reduce the overall time to process and litigate claims—ultimately making the process more efficient.
Detect Claims Fraud & Identify Unsafe Driving Behavior
The ability to more accurately identify accidents allowed the insurer to uncover scenarios of fraud and unsafe driving activities during claims events, including a driver and passenger switching seats and texting and driving.
Improved Customer Experience
Real-time alerts of potential accidents allowed the insurer to proactively reach out to drivers to inquire about their well-being and see how they could assist them in processing their claims—ultimately improving customer experience.
More Accurate Loss Forecasting
By identifying accidents precisely when they happen, the insurer could predict their potential monthly losses more accurately.