Vice-President & Director Product Development
First American Harvard Design and Mapping,
United States of America.
Prabhudoss Robert Samuel Senior Software Engineer
First Indian Corporation Pvt Ltd,
First Indian Corporation,
The property insurance industry uses many parameters to arrive at decisions on the amount to be assured, the loss claims from customers, detection of fraudulent claims etc. These processes are further complicated by the natural hazards that occur in and around the property.
To assist in this process, there exist multiple internet-based data feeds from the federal government and the private sector. These data feeds provide
Historic and real-time spatial information about calamities such as rain, winds, lightings, tornadoes, flood, earthquakes etc. Those data feeds are scanned repetitively and converted to spatial data with some expertise in the fields of Meteorology, Seismology and other earth sciences.
It would then be easy to use these data in any of the GIS software that maps the geographic locations of properties of interest to arrive at the following answers for example.
- Was the property really hit by a hurricane?
- Was the hurricane bad enough to cause damage?
- What would be the effect to the property – based on the intensity of hurricane?
- Was the sum assured optimal?
- Does this property fall under the zone of hurricanes?
Thus, the insurance companies would do better in their business if they could supplement their decision making with a GIS that uses spatial hazard data on top of their property maps.
This paper intends to outline a Geographical Information System (GIS) for ‘Impact of Natural Calamities in Property Insurance’. It explains the use of GIS in the process of Underwriting and Claims processing. The audience for this paper would include people from GIS, Insurance, Meteorology and Disaster Management.
The client (or) the policyholder insures his property with an insurance provider for an assured sum and keeps paying premium against it. When there is a loss or damage to the property, the insurance provider is bound to pay the policyholder.
Risk to the policyholder here is that he can keep on paying the premium until the policy period expires. But the risk to the insurance provider is much more as loss (or) damage to the property will result with the insurer providing the sum assured to the policy holder.
This, when taken in terms of multiple loses / damages, would be a big financial loss to an insurance company.
Underwriters identify and calculate the risk of loss from policyholders, establish who receives a policy, determine the appropriate premium, and write policies that cover this risk. Along with factors like crime, title, and human behaviors that would affect a policy, one of the main factors would be the natural calamity.
Natural calamities strike with (or) without warning posing a risk to the property and thus directly affect the Insurance Company. An underwriter can use a GIS System for natural disasters so as to take better decisions on the policies and thereby greatly reducing the financial risk to the Insurance provider. A large variety of historical datasets are available, which provide some indication of the likelihood of a specific hazard event happening to a property location. For example, data is available that plots the tracks of hurricanes over the past 150 years. Overlaying this historical data over property locations of potential policyholders would allow the underwriter to better assess the likelihood of a future hurricane event striking the property.
Claims are placed by the policy holder in event of damage to the property. The Claims Processor is responsible for processing the claims.
However, there can be fraudulent claims stating that the property got damaged by a disaster when the actual reason could be something else that is not covered in the policy – or perhaps the property has not been damaged at all!
Thus, the policy holder can trick the insurance company quoting a natural disaster that occurred in the vicinity and claim successfully. Such fraudulent claims are again a major reason for losses incurred by the insurance company. If there was a GIS with the actual disaster data with the Claims Processor, there is no doubt that these cases can be handled more effectively.
“The Insurance Information Institute estimates that fraud accounts for 10 percent of the property/casualty insurance industry’s incurred losses and loss adjustment expenses, or about $30 billion a year.” https://www.iii.org/media/hottopics/insurance/fraud/
6.Intervention of GIS
Some of the natural disasters that would pose risk to a property are as follows – Storm, Tornado, Hail Storm, Earthquake, Precipitation and Flood. All of these disasters are available with some form of data on the net.
6.1. How it Works?
We will see with an example from the Hurricane data source that is available at https://www.nhc.noaa.gov/ – this is the US Government’s National Hurricane Center website. The data source is a text (.txt) file and is the forecast advisory bulletin released for 4 times a day at an interval of 6 hours.
The data can be gathered at regular intervals, with multiple processes set to hit the data sources at preset intervals or delays. The data source is downloaded by this mechanism.
Based on the spatial information it has, GIS data is generated out of it.
Such data gathering will result in a large repository of historical GIS data that can be overlaid on the Properties portfolio. As an example, we will be taking the example of “Hurricane IVO” that hit the Pacific coastline of the Mexico during September 2007.
Figure 1 is the text data of a specific bulletin for “Hurricane IVO” that is taken as sample. Refer Figure. 1 [Edited for simplicity].
The logic of conversion depends on the natural calamity itself. It requires some knowledge of Meteorology to convert this. The parameters available for different calamities in the data stores are persisted as attribute information in the GIS data.
From the above sample, the following GIS information can be obtained.
A. The current position / eye of the storm is at 19.0N and 113.5W and can be generated into point geometry. Refer Figure 2 a
B. From the current position and the available forecast positions, it would be possible to get the possible path of the storm for next few hours as line geometry. Refer Figure 2 a
C. The current extent or area of the storm covered can be formed by the quadrant information available as polygon geometry.
i.e. 64 KT……. 15NE 15SE 0SW 0NW.
50 KT……. 60NE 30SE 10SW 25NW.
34 KT……. 75NE 65SE 30SW 60NW.
For example the first line in the above data means that there are winds with speed of up to 64 knots around the eye of the storm spanning 15 NM [Nautical Miles] to the Northeast, 15 NM to the Southeast of the current storm position. The 50 KT and 34 KT winds are flowing in their corresponding wind area. Refer Figure 2 b
D. With the quadrant information available from the forecast sections, we can build multiple wind areas. Connecting all 64 KT wind areas (or) 50 KT wind areas etc., would give us GIS information of the movement of a specific wind area across the forecast periods. Refer Figure 2.c
E. Both the forecast and outlook information can be combined to generate 3 day and 5 day forecast cone as polygon geometry. Refer Figure 2.d
The following steps can be performed to use the disaster data
a. When a request for a policy reaches the Underwriter, apart from other decisions he makes, he will look at the risk posed to the property due to the natural calamities.
b. He will load the property / parcel that is available as spatial information and then overlays the historical and the current disaster information that is available in the GIS.
c. He gets a clear view of the history of the property with respect to calamities. He can now answer questions like.
- What would be risk to this property due to hurricanes?
- What is the history of this property with respect to hurricanes?
- Does it fall under the zone of hurricanes?
- If yes what is / was the frequency?
- Has the property been hit by a hurricane ever?
- Was the hurricane bad enough to cause damage?
- What would be the effect to the property – based on the size of hurricane?
- How much can be the sum assured?
d. When the property is analyzed with all other disaster data, the underwriter comes to know about the threat of nature to the property and then decides on the policy accordingly.
A sample overlay is shown in Figure 3 for a single hurricane – Hurricane IVO. The area with horizontal lines shows the forecast wind area of the hurricane in a zoomed – in view overlaid on the parcels / properties. The results that arise out of this analysis would give a different dimension to the decisions taken by an underwriter / claims processor
The same scenario can also be applicable while processing the claims by the Claims Processor and one common question that would arise is – Was this property really affected by the hurricane?
7.Other Data sources
The data of all these calamities are available in various formats from various sources. They could be in Text (.txt), Comma Separated Value (.CSV), Shapefile (.shp), Grib (.grb) and NetCDF (.nc) formats etc. Though all of them might not be in spatial format, they have some attributes or the other which can be converted into spatial information. The source of data could be http site, ftp site and through third party vendors.
Some data sources are
a. The hurricane surge information in form of Grib files.
b. Provides the NetCDF and Shapefile formats of daily precipitation in the USA and Puerto Rico.
c. CSV files for Hail, Wind and Tornado information.
The above given information are only samples and we can get information about wild fires, earthquakes, lightning too.
It is no doubt that the Insurance market is facing heavy competition. At this situation the companies that have a technological edge over the other companies would always do well. GIS with its ability to break into any market would be one of the best technologies to be adopted by the Insurance industry for better performance.
This document acknowledges the support of the following persons
a. Mike Schmitt,
First American Flood Data Services.
b. Jesse Griffis,
First American, Harvard Design and Mapping, USA.
c. Vinaya Sathyanarayana,
First Indian Corporation.