Most insurance companies were founded 100 years ago or more. That kind of tenure can be a double-edged sword: sure, the expertise is there, but what about a taste for change? The vision and desire to do things, well, better?
The truth is the home insurance industry at large is still stuck in the past. While waves of insurance innovation have come and gone, many insurers still have yet to adapt new data sources that allow for better pricing and underwriting.
That may spell trouble for you, the homeowner, as homeowners insurance costs continue to climb. The problem? For those legacy carriers, information about your area may play a bigger role in your premium than the details about your particular house do.This isn’t always true, but it can impact how much you pay for your insurance.
So we do things differently. We use new insurance data sources to help us provide more accurate and fair pricing. This lowers overall homeowners insurance costs and helps us serve communities that need reliable, affordable insurance options.
Let’s take a look at how granular insurance data can make a big difference in your experience and the price of your coverage.
What is data used for in insurance?
To fully understand how new data makes insurance better, it helps to understand what insurers consider when evaluating your home for risk. Typically, your home insurance premium is based on a lot of factors, such as:
-
Your home’s location. Your location tells us a lot about your risk – like how likely your home is to experience an event like a wildfire or robbery. It also tells us how easily you can access community resources that can help mitigate the severity of a loss (like your proximity to fire stations).
-
Your home’s construction and characteristics. The age and construction of your home can tell us how well it will endure a big storm or other catastrophe. For example, homes with hip roofs usually better withstand hurricane winds than other roof shapes.
-
Weather patterns. Historical wind, rain, tides, and storm events in your area can impact your chances of loss.
Granular data helps us get extremely precise insight into these variables instead of working with general data points. Essentially, the more precise data an insurance company has, the more accurately it can assess risk.
How the old way of assessing risk increases rates
Yes, all insurers use a lot of information to help price their policies. The problem is those rating models are then attributed to entire zip codes or even states.
That means even if your home has less risk, its baseline pricing is going to be the same as your neighbors.
Historical models don't necessarily factor in characteristics that make your home less risky. For example, say your home sits on top of a knoll in a flood zone. Your home’s elevation affects both homeowners and flood insurance. Your home's elevation might reduce the risk of water damage, but that difference isn't necessarily reflected in your price if other carriers aren't using granular pricing algorithms.
But when insurance companies embrace technology, everyone wins. Aerial imagery gives detailed information about the property characteristics and the condition of your home. These data points help us better underwrite risks. Using big data, insurance companies don’t just gather information – they compile it into useful details that algorithms run to accurately assess risk.
How granular data helps serve underserved regions
Storm surge puts approximately 1.3 million homes at risk along the Gulf Coast and Atlantic seaboard. And that’s just from a Category 1 hurricane. A Category 3 increases that number to about 4.5 million. This historically makes finding home insurance difficult and expensive.
The same can be said about wildfires in California: 350,000 Californians have to get coverage from the FAIR Plan because they’re unable to get traditional fire insurance policies.
Granular data is a solution for this because it helps us see the real risk each home has, and that enables us to insure more homeowners in catastrophe-exposed areas. We use aerial imagery and thousands of data points to gather information about each home and determine risk within the home’s neighborhood. So while Home A is still in a higher risk area, it might have significantly decreased risk because of its surrounding topography.
By fairly assessing each property, we can manage risk better and offer lower rates.
Using granular data to reinvent the customer experience
When insurance uses big data, it creates a better customer experience all around. At Kin, we use granular data to help us quantify the frustration many folks have experienced with other insurers, from not being able to get insurance to sky-rocketing premiums to horrible claims experiences. (Think insurance claims are always miserable? Check out one member’s story after Hurricane Idalia.)
In addition to helping us create new products, cut costs, and operate in high-risk regions, we use data to:
-
Find out what is important to homeowners when it comes to insurance coverage and protection.
-
Tailor our marketing to your preferences.
-
Optimize your experience by resolving issues quickly.
-
Remove inefficiencies in both applying for insurance and processing claims.
-
Reduce company overhead and pass savings onto our customers.
Using data and technology to improve the customer experience doesn’t have to be earth shattering, either. Something as simple aerial imagery can help find the most affected areas after a major catastrophe, and using SMS (text messages) to check in on customers – like we first did after Hurricane Irma – can get the right resources to the right places and jumpstart claims sooner.
Getting new data sources in place takes some maneuvering and updating old systems, but we think it’s worth the effort. What’s more, our customers seem to agree.
