underwriting process. So, that’s the logic behind it.
McDONALD: You and I have both had advance looks at the presentations. They’re good. They’re very meaty and I think people are going to enjoy this. What I’m going to do now is I’m going to turn the presentation over to Glen Daraskevich. He’s senior vice president at Karen Clark and Company.
DARASKEVICH: Thank you, Lee, and good morning everyone. The focus of my presentation is to providing specific examples of how mobile technology is being leveraged to improve underwriting decision making specifically for the property lines of business. I’m going to start by first considering the data quality issues that are currently confronting carriers. Then I’ll spend the remainder of my time demonstrating how technology is being applied to address these issues.
At the end of the day we all know good underwriting depends on having access to good data. In the simplest of terms, not only do underwriters need the right data, they also need it in the right place and at the right time so they can easily access it when they need to make decisions.
Let’s start by considering does the industry have the right data today? Well, the data carriers collect is founded on the fire hazard. For many decades this made a lot of sense as fire was the leading cause of loss to property at the time. However, as population has shifted to the coastlines and other regions prone to natural peril activity, paid losses are increasingly dominated by catastrophes.
This really became evident during the hurricane season of 2004 and 2005. Most notably Katrina, Rita and Wilma -- or KRW. These events really exposed some significant data issues for many carriers as they realized they really weren’t collecting the right information necessary to manage catastrophe risk and the data they did collect had quality problems.
Across the bottom we see the types of data typically collected. We need to know where the risk is, its value and specific attributes about the property that will help us size it. Here, the dark blue bars indicate data the industry has always collected. The size of the bars on this chart are approximate guides as to how useful this information is in estimating cat losses. Since territory, construction and occupancy were the primary ingredients that influenced premium coverage for traditional perils, today all carriers have this information. Unfortunately, KRW confirmed the data typically collected – the data elements I’ve shown here in blue – is of limited value in estimating potential catastrophe losses. For example, loss experience revealed that many carriers, especially the commercial writers, were using insured limits to estimate their premiums. Using limits or understated replacement values and cat models significantly underestimated their loss potential and this was an unpleasant surprise for many companies. Post KRW, improving replacement value information has become a top data initiative across carriers.
The other revelation from KRW was that a property’s construction and occupancy were less important to loss potential than previously thought. What I’m going to do is explain this with some specific examples which I think will illustrate the point.
The following slides are from post-disaster surveys that were conducted for Hurricanes Gustav and Ike. However, I’ve conducted damage surveys for several events including KRW and I can tell you that a similar story can be told for each of those events. Now when you enter a neighborhood after an event, some people have a mental picture that each home will have relatively the same level of damage. But what you often find in the field is that just a small portion of the home in any one neighborhood will be damaged. Really, what we explore during the damage surveys is what’s causing these losses or what’s the primary driver for the catastrophe loss.
On the top of this slide we have two homes in a residential neighborhood right outside of Baton Rouge, relatively new construction and there is absolutely no physical damage upon inspections of these homes. But what we did find in these neighborhoods was a fraction of the homes had a specific garage design, which were double wide garage doors with no exterior wall behind it. I almost think of it as the most expensive carport you could possibly design.
What we saw was every single one of these homes
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