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High Hive for telematics in B2B insurance
#UBI #telematics insurance #insurtech
Feb 24, 2022
Telematics Insurance, UBI, the Hive
As we follow the industry news, we’ve come across the Hive Think Tank forum video. There, the global leaders in insurance, ride-sharing, and venture capital discuss the future of insurance in B2B, B2C, and B2B2C mobility, focusing on telematics and UBI. As it’s our favourite topic, and the Hive’s event itself was a blast, we can not but reflect on their ideas and share our views.
The video itself covers ride-sharing insurance and UBI 360 for the final mile and logistics. Both are relevant to us, but today we’ll discuss the second part, as it concerns UBI and telematics insurance for B2B, B2C, and B2B2C.

Brandy Mayfield — Chief Underwriting Officer at InShare — hosted the session with the participation of:

Let’s recap the discussion and highlight the insights.

Telematics in insurance is not equal to UBI
For decades, telematics has been utilized for vehicle management, and there were numerous attempts to adapt it for vehicle insurance. Unfortunately, with little success. As a result, people see UBI — the first and simplest telematics use case in insurance — as the essence of telematics-based vehicle insurance.
In reality, telematics insurance is not limited to mileage tracking. It’s about understanding how a driver utilizes the vehicle and the conditions affecting it.

The failure of telematics in B2B insurance is predetermined by data diversity
Speaking about the major inhibitors of telematics penetration in commercial lines, Pranav from Swiss Re stated that “the fragmentation in all steps of the data value chain creates a lot of complexity.” And this issue has not been solved so far. The variety of data sources, formats, quality, and precision all contribute to data diversity. All the panellists agreed, that the more connected vehicles appear, the more data they generate and the more fragmented it becomes.
Before we completely move towards OEM data, we should learn how to deal with fragmented data and then, on a scale, understand the correlation between telematics and risk. Only then we can talk about the effect of telematics on the loss ratio.

Telematics is the future of insurance
In the end, the panel recognized telematics data as an integral part of future insurance. And in order to make it work, insurers and telematics companies should collaborate to start reading and understanding this fragmented data landscape.
First of all, we should thank Pranav Pasricha and support the Swiss Re initiatives. Working on, as we think, the major problem in commercial insurance, we stand by every word of Pranav:

“The variety of telematics data sources and types is the main obstacle for introducing telematics-based service on a scale.”

After countless attempts and failures to introduce telematics in insurance, we ended up with a toxic image of telematics in the industry and a highly fragmented data landscape. And this data is as diverse, as it’s difficult to get, understand, and work with.

Speaking about the diversity of data sources, we agree on the problem of data quality and resolution, i.e., how rich the data sets are and how frequently the information is updated.
For example, mobile apps fail to provide rich data, unlike OEMs that generate hundreds of parameters. At the same time, aftermarket telematics providers offer fewer parameters with higher frequency or resolution.

Poor resolution and frequency affect risk assessment, and we should either learn how to enrich it or wait 15 years till OEM embedded data is on the scale and affordable.

The issue of telematics data implementation
One more aspect to consider is how insurers use telematics data for risk assessment.

Traditionally, people associated telematics insurance with UBI, or mileage-based insurance, simply because the information about mileage is easy to extract and utilize. That’s why UBI has been on the agenda for years.

When it became clear that mileage alone is not equal to vehicle risk, telematics insurance scaled to behaviour-based models, that take speedings, brakings, and accelerations into account.

Still, one more data set is often neglected, although being an integral part of the game. And this is the contextual information – geography, road types, weather, and other conditions.

Only when we understand how the driver uses the vehicle, performs at the wheel, and under what conditions, we can build an effective risk assessment model. And this is, we believe, is the only efficient way to use telematics in this context.
Sum it up

If we want telematics data to work efficiently for risk assessment, we should first learn how to harmonize, normalize, and enrich telematics data on the scale. Then we can use the information to build usage-, behavior-, and context-based risk assumption algorithms powered by AI.

We believe that this is the only right way to use telematics for vehicle insurance, i.e. improve loss ratio, figure out competitive pricing, and achieve better claim management.

Being on the same page with the panellists, the Draivn team strives to override the main inhibitor of commercial vehicle insurance – data fragmentation in terms of formats, resolution, and quality.

And we look forward to joining our forces with other stakeholders bringing vehicle insurance to another level and making roads safer together.

Next week, we’ll share our thoughts on ride-sharing insurance, being the second topic of the Hive Think Tank forum. Among other things, we’ll elaborate on what Draivn offers to insurers and brokers who want to roll out propositions for ride-sharing, gig drivers, last-mile delivery companies, etc.

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