1. Charles K. Huyck EVP, IMAGECAT www.imagecatinc.com
Inferring Global Exposure Databases (GEDs) from Remotely Sensed Data Authors: Charles Huyck, Zhenghui Hu, Robert Chen, Greg Yetman
Charles K. Huyck
EVP, IMAGECAT

2. Alright, we are going to change gears here and go from talking about some robust products and services that have been launched to some active research. One of the things we pride ourselves on at ImageCat is our ability to look forward to emerging technologies and areas and anticipate applications for the insurance sector- and in fact, the Inhance program was just such an effort funded by the technology strategy board. This effort, funded by NASA is an applied sciences program grant aimed at brining research to practice. We received a grant in 2013 which we completed- and have been refunded by NASA through 2018 to combine their data with other information to create a “GED”- or global exposure dataset- for the purposes of making decisions in the Insurance industry. Based on our preliminary discussions, we have come to the conclusion that this data would be useful as a disaggregation tool- and based on that feedback we have put together a pilot demonstration to solicit market feedback.
If you are interested in this capability, I encourage you to talk to Gavin about becoming part of our early adopter program- which has a cost share element- both in terms of cash support and in kind labor.

3. Sample number of buildings
Moment Frame
Over 7 stories

4. Benefits of better exposure data…
Global Default
Exposure
Effect of higher premiums
in emerging markets
Exposure Modeling
Population
EO Exposure
EO data
Price
Higher premium to
cover uncertainty
Decision
Support
Lost
benefit
Demand for insurance
CAT Modeling
Insurance sold
Underinsurance
Hazard
Vulnerabilities
Calc.
Prob. Loss

5. The promise of GIS and crowd-sourced data

6. The promise of GIS and crowd-sourced data
Skews risk
Diverts resources towards known assets
Fails to adequately capture vulnerability
Difficult to come by
Rapidly obsolete

7. Buenos Aires, Argentina
PAGER/WHE
Very High Urban
High Urban
Urban
HD Res
Res
Rural
Urban_Non_res
Rural_Non_Res
Steel
0.01
0.03
Ductile reinforced concrete moment frame
Low-rise
0.06
0.05
0.12
0.02
Mid-rise
0.15
0.11
0.08
High-rise
0.38
0.30
Nonductile reinforced concrete frame with masonry infill walls
0.10
0.17
0.04
0.09
Reinforced masonry
0.20
0.33
0.43
0.35
0.4
0.5
Local field stones dry stacked (no mortar). Timber floors. Timber, earth, or metal roof.
0.13
0.22
0.18
0.25
Unreinforced fired brick masonry
0.14
0.37

8. GADM 2 Weights
So we have run the algorithm for a large region of China as a test, and essentially isolated industrial areas and assigned them each a weight- for a given unit of geography. So with these areas the circles are quite small- because there is a lot of industrial activity here, and in these areas they are quite large. Since there are fewer of them, each one carries more weight.

9. So with that, I will jump over to this Pilot running on our servers here in Long Beach. This is what we anticipate the functionality might eventually look like- we allow users to identify a region, a occupancy type, and a total exposure. They choose an Inhance report template- click submit, and a minute or so later, they are able to bring this type of information up.

12. Preliminary classification of urbanization zones in Southern California including industrial (blue) rural (green) residential (yellow) high density residential (orange) and various degrees of mid and high-rise (intensity of red). These classes will be used to identify development patterns for replacement cost estimation.

13. Questions?

14. Reducing Uncertainties
Here is a typical example - Single site basis – where an assessment of building performance might be improved by augmenting information for a particular building.
Uncertainty on left is due to unknown exposure. Large number of distributions due to uncertainty of construction type.
However if we can improve the exposure data then the uncertainty is reduced which has ongoing benefits for underwriting, capital modelling, etc
Inhance provides transparency to provide alternative view to the black box environment.
SO why is this important?
How about emerging markets with inherently poor quality data?