The hurricane is close. Is your location data close enough to reduce insurance pay-outs?

In the insurance industry, even close enough is often not good enough. Take the recent Hurricane Florence, for example. As it lazily rolled through the Carolinas, it caused billions of dollars in damage, forcing insurance companies to write billions of dollars in payouts.

Tue Oct 09 13:16:00 EDT 2018
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In the insurance industry, even close enough is often not good enough.

Take the recent Hurricane Florence, for example. As it lazily rolled through the Carolinas, it caused billions of dollars in damage, forcing insurance companies to write billions of dollars in payouts. Some of these payouts might have been unnecessary had the premium writer known, for example, exactly how close a property was to a flood plain.

It’s simple: hyper-accurate location data can save insurance companies money.

What is hyper-accurate location data?

And how can it ensure more accurate premium pricing?

Currently, industry standard location data for homeowner policies typically relies on interpolated street data, meaning that streets are split into segments of varying length, and homes within that segment are priced at the same risk. The more precise method, however, is to use latitude and longitude measured in the center of the parcel, where the house is. That can be a difference of a few feet from the segment, or it can be a difference of 500 feet, a mile or more, depending on how good the data segment is.

When underwriters can more accurately assess the risk of a location—whether it's where a home is located or where a car is garaged—policies can be priced according to the risk that location actually represents.

It is not, however, a zero sum game. With over-priced and under-priced scenarios, the chances are that a good deal of underpriced business will be written. The key point is to reduce underpricing, because when the underlying data leads to policies that are priced at a lower rate than they should be, not only does it open an insurer up to paying out on a policy it hasn't received adequate premiums for, but underpriced policies may also end up constituting a larger and larger portion of the overall book. This is essentially adverse selection.

Sometimes no change in pricing, but in affected cases, off-pricing is dramatic

In a study we (Pitney Bowes) commissioned to demonstrate to insurance companies what the potential return on investment would be associated with switching from their current applications to a new master location data geo-coding application, the actuarial firm found that although most policies would experience no change in pricing, in the 5 to 10 percent of cases that would, the range of under- or overpricing in premiums was significant. Some homeowner policies, for example, were underpriced by as much as 86.7 percent, or $2,000 a year per policy.

As I mentioned, hurricanes are a prime example of these principles in action. The study found that underpricing is actually worth more than $100 million in lost premiums in the state of Florida alone. And while all those losses might not be strictly due to hurricanes, the data is staggering.

How can insurers more accurately assess potential higher-risk locations?

Precise parcel level data--using latitude and longitude measured in the center of the parcel, where the house or garage is located—can help more accurately price policies according to the risk that location actually represents.

Insurers must ensure they are using authoritative resources to obtain accurate location data and establish a unique identifier for every location in America. The increasing need to exchange accurate information requires an architecturally sound way to exchange data. When selecting data sets, it’s important to ensure interoperability among the sources so, for example, an aerial view can be overlaid with a county-provided building outline.

And finally, of course, data quality is not a one-time deal. To ensure the accuracy and credibility of a data set, it must be regularly monitored and maintained.

The pay-off is in the hyper-accuracy of the data

Wildfires are burning. Hurricanes are blowing and flooding. Tornadoes are whirling. Nature at its worst is continuing to cause massive destruction and demand huge insurance pay-outs.

For insurance companies, it is imperative to be able to uniquely identify the properties they insure to minimize the inevitable destruction causes to their bottom line.

Learn more:

Join industry experts for an informative on-demand webinar titled Pinpointing the Issue: Why Hyper-Accurate Location Data Can’t be Overlooked in Insurance to learn how data improvements can help prevent losses and avoid underpricing.

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