How pattern-based AI is transforming driver risk management
Get ahead of the competition with a new perspective on driver risk
For many years now, telematics has become something of a “go-to” solution for many fleets who have recognized the safety and financial benefits of incorporating data on activities such as vehicle location, driver behavior and fuel consumption into their driver risk management programs. But the needs of fleets are changing fast, to accommodate factors such as mobility-as-a-service, global sustainability efforts, and ever-increasing internal pressures to improve efficiencies while saving costs. To stay ahead of the competition, telematics services providers are having to advance even faster.
A changing fleet landscape
It’s difficult to describe a “typical” fleet today, due to an increase in mobility options – such as car subscription and car-sharing services. Of course EVs are also starting to make up a larger proportion of many fleets. On top of that we are seeing more organizations working within the gig economy, and in middle-mile and last-mile delivery. Many of these new fleet types use self-employed drivers in their own vehicles. And, it’s only a matter of time before autonomous vehicles – or at least vehicles with a multitude of anonymous features – are the norm for fleets.
With this changing fleet and mobility landscape, telematics service providers cannot afford to stand still. Thankfully, AI’s cutting-edge capabilities are continuously improving our ability to understand how the vehicle, the driver, the road, and the environment intersect to provide a more accurate individual driving measurement for fleet operators, personal vehicle owners, new mobility providers and insurers.
Pattern-based AI doesn’t rely purely on driving events
Driving behavior follows patterns, and today’s technology can start rapidly matching those patterns against almost 7 billion previous trips. Pattern recognition is a giant leap forward from relying solely on “event-based” measurements such as harsh acceleration, braking, and cornering. These provide isolated snapshots of a driver’s behavior and are often a source of friction between end users and telematics service providers. Events can even be wrongly interpreted – for example a telematics solution might record a harsh braking event as negative, when the event was actually the result of a driver slamming on the brakes to avoid hitting a child who ran into the road.
Pattern-based AI measures every second of driving against billions of previous trips, providing an overall picture of risk that doesn’t rely on isolated events. It is also consistent across all vehicle types and all locations, so the measurement of risk and how events are generated does not vary between different road users, or different solution providers. Crucially, pattern-based profiling can also be layered onto existing solutions. This adds per-second data on top of driving events – you could think of it as an enhancement layer that fills in the blanks.
Early risk identification and accurate insurance pricing
For fleets, pattern-based AI using real-time data delivers multiple potential benefits, including early risk identification and accurate insurance pricing. Typically, 20% of a fleet’s drivers will account for 80% of their risk, so accurately identifying the 20% riskiest drivers and successfully acting to reduce risk levels can deliver a significant return on investment.
Individual pattern-based profiling also enables risk and safety managers to measure and compare driver risk levels at any time, regardless of vehicle type or location. This helps ensure timely implementation of risk reduction tools, such as training and/or coaching. The accurate measurement of CO2 emissions also ensures fleets can identify, and report on, their environmental impact, and demonstrate how improvements in driving behavior contribute to CO2 savings.
It’s not just fleets that can benefit
A pattern-based approach to driver risk management also has huge benefits for insurers. Overall, there is a growing acceptance of in-vehicle communication systems to track driving behavior and mobility-as-a-service is a key trend boosting the demand for usage-based insurance for personal lines insurance. However, adoption rates for commercial motor coverage remain stubbornly low. The main barrier is the objection over driving events and how they affect price. However, aggregating single events into driving patterns overcomes most, if not all, these objections and provides insurers with a transparent, easy to understand communication tool, reducing the “mystery” of insurance pricing for customers.
Arming insurers with more decision-making power also gives them a competitive advantage in that they can offer products instantly, no longer having to pore over variables to estimate risk levels. This can be especially useful in a situation such as an unforeseen crisis or pandemic. Rapid changes in market conditions require rapid responses.
When data is accessible on an individual level, it is easy for insurers to create attractive, personalized offers to incentivize and reward loyal customers, including fleets. This can be a crucial factor in increasing customer acquisition and winning customers from competitors. It can also make fleet customers more likely to renew their policies with their current provider.
Pattern-profiling can drive safer behaviors
Using a pattern-based measurement of driving behavior is fundamentally good for road safety. That’s because solutions that include pattern-based AI give fleets a clear path to reduce risk and therefore their risk-based pricing. Put simply, the fleets with the safest drivers pay less for their insurance, meaning there is a financial incentive for fleets to encourage safer driving behaviors.
Because individual drivers can see how their own risk levels and CO2 emissions compare with those of their colleagues, and are engaged through gamification features and rewards, a pattern-based approach to driver risk management helps influence safer driving behaviors at the individual level. In turn, this supports global road safety and sustainability efforts, including the United Nations’ 17 Sustainable Development Goals.
Sustainability has become an urgent business goal for fleets. Fleet management companies (FMCs) and telematics service providers (TSPs) have a unique opportunity to grasp the latest technology and race ahead of the competition in fleet sustainability management.
The prospect of a motor insurance revolution is a hot topic and there is currently much discussion around how technology is finally transforming the industry. But it’s not just motor insurance companies themselves that are part of the revolution, as other major players discover that by using their GPS data for risk analysis, they become part of the next generation of motor insurance influencers and providers.
By connecting 200,000 cars to Greater Than’s risk analysis platform, ABAX managed to identify new market entries in less than four months, segmentized target groups, and develop a tailored insurance offering for its fleet customers.