The world has become more connected and data-intensive but only marginally smarter.
Cars are a good example. They've been connected for some time and are only now becoming more autonomous and contextually aware, capable of journey and self-management.
In this latest phase, manufacturers treat cars as ecosystems that manage everything from improving journeys to maximizing vehicle uptime and minimizing disruption. As a result, insurers are now faced with many opportunities and choices. With this comes threats and challenges in equal measure.
The question many ask is, as car manufacturers exponentially increase their understanding of their products and the people driving them, will they become the insurer? A better question is, will the insurance industry adapt quickly enough to avoid this outcome?
I hope so. There are huge upsides for insurers and their customers. However, a fundamental shift in the way insurers align their businesses and their technology is required.
With a raft of new
There are already solutions in the claims automation space that handle 'upstream' data like issue detection and address the really difficult stuff like slow collisions. This includes how they are modeled and validated. There are also those that deal with 'downstream data' into repair networks during the claims process itself. All of which support a better ENOL service and claims experience overall.
The problem is whilst the
Therefore, insurers often rely on a crash reconstruction specialist to make sense of the data, ranging from $2,000 to $5,000 per claim (making this largely unfeasible). The situation is made worse because there are many different types of EDRs, which can vary greatly in the data they gather from one system to another. Moreover, the nature of a crash can mean that certain data is not recorded, making EDRs an imperfect system even at the best of times.
Another source of intelligence that insurers use when investigating a claim is the First Notice of Loss submissions from the insured parties involved in the crash. These will typically include a brief description of the crash, the resulting damage, police reports and any photo or video evidence. This extends to services now categorized as "estimatics", which embodies tools, data and services that can better estimate the damage and repair requirements in a more efficient and largely automated way.
However, biases and imperfect memory can skew the accuracy of these reports. Even crash reconstruction specialists can differ in opinion when interpreting more subjective information such as road conditions, weather and the activity of third-party vehicles moments before a collision.
So these solutions have a very clearly defined problem space. However, this does mean the data source has to be proven, especially if there is any legal dispute and arbitration involved. This is becoming a major factor in insurance fraud, where things like
Another important area to assess is the solutions that sit on tech outside of the car (i.e. via the mobile or another connected device). Insurers must determine their likely uptake with customers, so they can ensure they get the level of data access and ongoing tracking needed to make the service work. Understanding adoption rates of device-level services is vital and a critical business case component for carriers.
Then there are the downsides of tech-driven service provision. The issue that has plagued this area has historically been consumer uptake. The
Many don't keep a car for long, and the installation invalidated the manufacturer warranty in some cases, and so on. The truth is "apps" aren't reliable either, what happens when the iOS upgrade invalidates some of the data security protocols? Does the insurer take the risk? How much can they make it a proper usage-based offering?
If the dawn of zero-touch, multi-choice and seamless car insurance is here, it begs the question, how do you prepare when ignoring it isn't an option anymore?
Without a clear view of which tech and partners to choose and how best to integrate them, there are a lot of potential challenges, many of which are complete unknowns, and these "bets" can look overly risky.
But, perhaps the biggest challenge is the legacy systems at the heart of most insurers' businesses are not set up for the kind of consistent adaption and customisation that will be required.
What's needed is a new system design built around a strong foundation of ecosystem enablement. A system that, like its e-commerce forebears, can provide the integration + data fluidity + rules-based system (high level of configurability) that ensures the insurer gets the capability needed to easily consume the value of these partners.
A good example is By Miles (recently bought by Direct Line Group in the UK), where the offering of the product is usage-based and uses mobile as well.
All this means utilizing a range of data sources, experimenting with them and creating new, more relevant business models for car insurance from how it's priced. This includes how the policy works, claims, downstream repairs & the settlement process. All of which demands a fundamental change in approach to system design and business model creation.
Right now, the reality for many insurance businesses is any change, no matter how big or small, is expensive and takes significant time. This means that every change needs to be thoroughly thought through. None of this bodes well for a speedy response to a car market that is changing rapidly.
The answer is to have the right foundations to adopt the ecosystem needed to take advantage of this emerging potential.
The car is transforming rapidly, the opportunities to work with the vehicle ecosystem have never been more straightforward, and a lot of the fail points of the past have been overcome. Insurers will need to adopt these capabilities and take advantage of the efficiencies, customer experience benefits and new proposition potential this will undoubtedly bring.
