You're likely familiar with the adage: “There’s no such thing as selling a bad risk. There’s only selling a badly priced risk.”
In an industry where risk is unavoidable, accurately assessing risk is paramount. The more accurately insurers can assess and factor for exposures, the more likely they can sell profitable policies. However, this is easier said than done.
Many insurers are forced to gauge risk based on data that’s too imprecise to guarantee accuracy. As street, address, and ZIP codes are tweaked to resolve duplicates and fix errors in USPS data, many rely entirely on the wrong data.
Finding the right balance between risk and profitability is nearly impossible without precise underwriting based on accurate geocoding and risk analysis practices. To keep loss ratios in check, insurers must develop new processes that help them arrive at the most accurate price point possible.
See also: "Micromorts": A New Way to Talk About Risks
Risk evaluation is a game of accuracy
One of the biggest mistakes an insurer can make is undervaluing or overvaluing the risk on a specific property. But it’s easy to understand how an inaccurate assessment can happen in practice.
When evaluating a property’s risk, a typical insurer may perform some internal modeling to gauge the losses and wins other insurers have already experienced in that specific area. For example, if an insurer is exploring potential policies for properties along the Miami coastline, they’ll attempt to gauge which properties have a higher risk of storm surge and which are more protected.
If an insurer overprices a policy (thus overestimating the risk exposure), they’ll struggle to sell that policy, and upon renewal may eventually see that policy churn to other companies that have more accurately identified the risk level. Conversely, if the insurer underprices a policy (thus underestimating the risk exposure), the insurer can sell that policy quickly but at a price that damages loss ratios. These underpriced customers are discouraged from leaving, leading to policy premium leakage.
But how do you factor in these hard-to-pinpoint risks without pricing policies so high that resource-strapped policyholders are edged out? In a time where there’s so much insurers can’t predict, the answer lies in fine-tuning the tools and processes that are in their control.
Accurate and high-speed geocoding, the process of turning a street address into geographic coordinates, offers a solution. Many insurers rely on outdated, on-prem servers and processes to assist their geocoding efforts. These servers are often slower and lack the precision of newer, cloud-based solutions, which in today’s insurance landscape, can make all the difference. For example, if an insurer is evaluating risk for properties along a Florida coastline, a discrepancy of as little as 50 to 100 feet matters during hurricane season.
By using high-speed geocoding capable of processing tens of millions of records per hour, insurance analysts can efficiently model various scenarios and identify the most profitable pricing strategies.
So, how can you reach this next level of underwriting, where you can consistently and precisely tie perceived risks back to policy pricing? It starts with hyper-accurate geocoding.
The next frontier in underwriting
There are four main levels of geocoding precision: ZIP codes, streets, parcels, and rooftops.
Each progressing level offers more precise coordinates, which makes rooftop-accurate geocoding the goal for all insurers. Using high-precision geocoding can improve risk analysis and assist insurers with improving loss ratios. Even minor imperfections in geocoding can lead to millions of dollars of inaccurately priced policies.
Rooftop geocoding is crucial for success, but equally as important is the ability to have a persistent and unique identifier (PUID) that acts as a single source of truth for a specific property. This is otherwise known as an address key. As street names change, you must maintain access to a stable address key over time.
By connecting risk data to a PUID, insurers can create and test risk models accurately, faster, and more efficiently. A PUID can also link multiple risk data variables to a single location, even if that location may be associated with different street names and addresses.
The first step is investing in more accurate geocoding and more consistent address data sources. But to effectively and consistently leverage these insights during underwriting, you must have rooftop-accurate geocodes and a PUID. This is where a cloud-based application programming interface (API) provider can come in handy, providing a customizable geocoding solution that fits neatly into your existing underwriting processes. The right API provider can help you manage and interpret data points from various sources, making it easier to leverage them in your underwriting process.
If you haven’t already done so, it’s time to explore a partnership with a third-party geolocation solution provider — because the most accurate data wins in insurance underwriting. You can have more experience, resources, and employees than the next company, but you won’t be able to maximize profit margins without accurate data.
See also: The Cognitive Biases Hurting Risk Management
Accurate property data is king
It’s time for a more nuanced approach to understanding location risk, one that prioritizes rooftop-accurate geocoding and a persistent, unique identifier for the properties being underwritten. This combination puts you in a better position to accurately gauge the risk for a specific property and, in turn, find the right price for that location.
Ultimately, for insurers, whoever has the most accurate and scalable geocoding will put themselves significantly ahead of their competition. Address and ZIP code data will continue to evolve, but those who prioritize hyper-accurate geocoding today will continue to reap the benefits for years to come.
