Our GM of Mobility, Ian Sweeney, Pops the Hood on Trov’s Mobility Insurance Platform
Written by Ian Sweeney December 17, 2019
Trov’s Mobility Insurance Platform enables emerging mobility services by providing context-sensing insurance to reduce total cost of risk through data. While you may have heard of our work with Waymo and Free2Move, you may be less familiar with Trov’s technology and how it works. In the following Q&A, Ian Sweeney (VP & GM, Mobility at Trov) offers a look under the hood of Trov’s mobility solutions.
Q: Insurance has been provided for hundreds of years without integrated technology. What’s changed and why should mobility services care?
A: Pressure on mobility platforms to get to profitability quickly has increased radically. One of the biggest expenses for those emerging mobility businesses is their cost of insurance. If they can reduce that cost quicker than their industry peers, they have a competitive advantage. The ability to manage risk and optimize insurance costs, however, relies exclusively on having the data.
The second change is the widespread availability of that data from vehicles, the environment, and humans. Trov’s integrated technology platform ingests the data to enable context-sensing insurance. Knowing the context, or “risk state,” of every vehicle in the fleet allows us to right-size the coverage continuously – down to the millisecond, resulting in our customers paying the true cost of their risk.
Trov’s platform also provides incident management tools for when things go wrong, from personal items left in a vehicle to more serious accidents that result in insurance claims. Trov’s technology combines the two datasets; the real-time risk state of every vehicle, and the precise data on where in the fleet risk is occurring to provide our customers their own unique risk profile. With a known risk profile our customers can act to mitigate and eliminate risk, and hence cost.
Q: What factors does Trov’s technology consider in its mobility risk assessment?
A: Trov’s platform ingests a wide variety of data to arm mobility partners with details specific to their mobility use case. This is a key consideration for mobility companies given the inherent differences between fleet types. For example, an autonomous vehicle provider requires drastically different data collection – in terms of both type and scope – than, say, an e-scooter fleet.
Generally, the data can be summarized in three categories:
- Real-time vehicle data – describes what the vehicles are doing, and where they are doing it. For instance, a vehicle is parked or being driven by a reliable driver.
- Risk data – describes when something bad has happened. For example, when a vehicle’s window has been smashed, or when an automobile has been in a collision.
- Environmental data – supplements the picture of risk. Traffic data, for example.
Q: Give us a sense of how Trov’s algorithm would adjust insurance premiums to reflect the current state of risk in a carsharing service.
A: Our context-sensing insurance is quite sophisticated in that it can handle an infinite number of risk periods, or states of risk in which a vehicle can exist.
In a simple example, a vehicle that is known to be parked has a low level of risk, and hence a minimal insurance cost. When that vehicle is in a driving risk period, it is more vulnerable and requires a higher level of coverage.
The Trov Mobility Insurance Platform tracks every vehicle in our customers’ fleets in real-time, to consistently adjust the level of coverage. Most of our customer’s fleets have at least five risk periods.
Q: Does Trov’s technology offer automated risk analysis or predictive analytics to help fleets avoid risk?
Traditional insurance amasses sufficient data over multi-year horizons to understand a fleet’s risk. Trov’s Mobility Insurance Platform builds datasets for our customers. In fact, data collection begins the very second Trov is deployed.
Our algorithms continuously apply pattern recognition to said data – offering key insights to identify issues like high-risk drivers and vehicle misuse.
For example, when someone rents a carshare vehicle tagged for personal use, Trov can easily identify if the vehicle is actually being leveraged for commercial operations. In such an instance, the misused vehicle is essentially uninsured – meaning the vehicle provider is liable. With this insight, a mobility company might respond by expanding coverage for their use case or cutting ties with irresponsible drivers.
Q: How does Trov collect real-time data? Do you insert hardware into partners’ vehicles?
A: Trov works with mobility providers offering transportation via digitally enabled platforms. We simply “piggyback” the data collection system that’s already integrated into our customer’s vehicle or app, to get a clean digital signal describing the vehicle’s risk context.
This method negates the need for us to insert any hardware in the vehicle – and avoids additional equipment costs!
Q: How does the claims process work?
A: Many of our partners are not used to dealing with clients calling them about insurance. Take our OEM customers for example. They’re used to selling cars and are evolving their business model to sell mobility directly to consumers – which means they can expect to provide customer service related to incidents.
Trov provides our customers with the tools to operationally manage their risk from handling a small incident, like a lost item, or a more serious occurrence that results in an insurance claim.
While the claims process is operationally important, it also has tremendous value from a customer service perspective. Some customers need lightning fast automated claims resolution, while others need a white-glove approach to ensure every last detail is handled correctly. Trov’s claims tools are configured to match our customer’s customer-service goals.
Q: Trov works with auto fleets, but can its technology be applied to other use cases?
A: Yes, absolutely. We work with many different types of fleets – from Waymo’s autonomous vehicles to Groupe PSA’s traditional car-share vehicles – and the approach is applicable to many use-cases where context sensing insurance is needed.
The sharing and gig-economies have created a limitless number of scenarios where varying levels of insurance are needed for assets. Our model can be adapted to provide protection to a scooter for the duration of a ride, or a person as they assemble IKEA furniture in someone’s home.
The opportunities are truly endless – and we can’t wait to show you what’s in store for 2020.
Did we skip over a burning question? Please contact firstname.lastname@example.org with any follow up questions, and we’ll be in touch!