Motor insurance pooled pricing in the age of connected data
Using crash probability insights to strengthen existing pricing models
The way that motor insurance is priced has changed over the last couple of decades. Thanks to data-driven insights, insurers have enhanced their pooled pricing models to encompass risk. Now, thanks to new artificial intelligence (AI) technology, insurers can take their pooled pricing models to another level, securing profitability into the next decade and beyond.
How pricing works in motor insurance
Pricing risk at a group level is standard practice in motor insurance. It involves grouping drivers into “buckets” based on multiple factors including age, occupation, address, vehicle type, and driving history. In general, two drivers of similar status will pay a similar amount for their motor insurance.
Telematics has introduced an element of risk-based pricing to motor insurance by providing insights into driving behavior through the identification of events such as speeding and harsh maneuvers. I’ve personally spent the better part of my life in R&D to find the best technology to determine the relationship between driver behavior and crashes. That’s why I’m so passionate about encouraging motor insurers to make the most of AI, specifically crash probability.
Diagram: Current motor insurance pooled pricing model
Data gives a new dimension to pooled pricing
AI that measures crash probability is a powerful tool in motor insurance. It’s capable of providing a new dimension to traditional pooled pricing models by segmentizing each bucket into high/ low loss cost.
With the industry currently facing intense cost pressures and growing competition from new players, the pricing model needs to perform. With crash probability, motor insurers that wish to improve their existing pooled pricing model can optimize it with new layers of previously unseen knowledge.
A minority of drivers account for half of the loss cost
A driver placed into a risk bucket earns their place in that bucket because of several factors. However, without a doubt, the drivers in each bucket still carry different levels of risk. That’s because of their attitude to risk and the decisions they make on the roads.
At Greater Than, our AI can identify the 15% of drivers who stand for 50% of the loss cost in each bucket. And just as importantly, the 85% of drivers who are most profitable due to sharing a common, lower risk attitude.
Diagram: Pooled pricing model enhanced with AI
Finely tuned pooled pricing delivers multiple benefits
For motor insurers, gaining a deeper understanding of their portfolio is empowering. It places them in greater control of all aspects of risk.
With new intelligence into crash probability, motor insurers can decide whether to renew or adjust customers’ premiums. They can choose to take action to reduce risk, for example, via gamification or incentive programs. They can also calculate their loss cost in an entirely new way. Without replacing their current pricing models, motor insurers have much more control over their whole portfolio.
Obtaining new predictive insights is easy
Motor insurers already have all the data they need. It’s just a case of accessing new layers of information from that data to enhance existing pricing models. With Greater Than’s AI, all that’s needed is GPS data that can be obtained from various sources including dash cam, smartphone app or other API/SDK.
Greater Than’s technology analyzes the data and identifies crash probability and cost for each driver. For motor insurers keen to increase their focus on sustainability, we can also determine the climate impact for each driver by measuring CO2 savings as a percentage and in grams.
To learn more about how AI can help motor insurers increase competitiveness and profitability without having to substitute their current pricing models, please contact Greater Than or book a meeting.
