Location Intelligence | Pitney Bowes

Precise location intelligence helps reduce insurer’s underwriting risks

Insurers build their businesses on their ability to accurately assess and price risk when writing policies and determining premium costs. Industry standard location data for homeowner and auto insurers typically relies on interpolated street data and location of garaging, respectively. What that means is that streets are split into segments of varying length, and homes within that segment are priced at the same risk.

Once again, however, technology – in this case, location intelligence data – has emerged to make things more accurate, hence more cost-effective and risk adverse. Jay Gentry, insurance practice director at Pitney Bowes, observes that the more precise method 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. For example, a segment could possibly include two differently rated flood zones and traditional data would not differentiate. Location intelligence data could specifically determine if one house was located in a higher risk flood zone than its neighboring home. "It just depends on how good the segment data is," says Gentry.

And that flows into pricing, because 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.

To learn more, read Close Enough Is Not Good Enough: Why Hyper-Accurate Location Data Matters for Insurance, a Forbes Insight report, sponsored by Pitney Bowes.

What difference does a few feet – more-or-less – make?

In many cases, the gap between the estimated and precise location is small enough to be insignificant, but where it’s not, there’s room for error – and that error can be costly.

Studies conducted by Perr&Knight for Pitney Bowes looked into the gap between the generally used estimated location and a more accurate method for insurers, to find out what impact the difference had on policy premium pricing. The studies found that around 5 percent of homeowner policies and a portion of auto policies – as many as 10 percent when looking at Zip5 data – could be priced incorrectly because of imprecise location data. Crucially, the research discovered that the range of incorrect pricing – in both under- and overpriced premiums – could vary significantly. And that opens insurers up to adverse selection, in which they lose less-risky business to better-priced competitors and attract riskier policies with their own underpricing. 

The value of hyper-accurate location intelligence

Precise location data helps insurers realize increased profits by minimizing risk in underwriting, thereby reducing underpricing in policies. These factors combine to improve the overall health of the insurer’s portfolio. Keep in mind:

  • “Close enough” is not always good enough. Even though location is close enough most of the time, imprecision can have big costs when it masks proximity to hazards.
  • The portion of policies affected may be small, but it can have big cost impacts. The range of under- and overpricing varied widely, with some premium pricing off by more than $2,000. And the impact of underwriting leakage is actuarially spread across the entire portfolio, making premiums incrementally less competitive.
  • Underpricing is not “zeroed out” by overpricing. In fact, it opens insurers to adverse selection, in which overpriced policies are lost to more accurately priced competitors and underpriced policies make up a greater proportion of the business.
  • Time to value can be quick—and new ratings filings are not always needed.

Learn more

Read about the studies’ detailed findings in Close Enough Is Not Good Enough: Why Hyper-Accurate Location Data Matters for Insurance, a Forbes Insight report, sponsored by Pitney Bowes. The report concludes with a few key takeaways for insurers going forward.