Using GIS in the Insurance Industry

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Location has a large impact for the insurance industry, as a variety of natural or even social risks, such as crime, could affect insurance rates for life and property. For hillside erosion and landslides, which are affected by not only areas that are in immediate risk, insurance companies can incorporate a variety of soil, subsurface structure, slope, and locations that are likely to be impacted in the event of a major landslides.

One software that is utilized is arcSlopeStab, a plugin for ArcGIS, which takes raster data and maps regions of likely impact by slope erosion and possible landslides using calculated physical factors and local geography.[1] Flood insurance uses a variety of techniques within spatial analysis to determine rate policy. Multiple regression modeling, that takes land use factors and rate of past flood occurrence in areas, to create probability maps where floods are likely to occur along different riverine regions.[2]

On the other hand, where health insurance is now required, such as in the United States, GIS is being used to find areas where rates of coverage is low or more likely to be at risk for not being covered, including factors (e.g., age, income, and health) that affect reasons for coverage. One study showed that electronic health records (EHR) are best utilized to locate patients who are uninsured and a spatial component is likely to be evident for uninsured people to be found near each other.[3] In other cases, studies also show that increasing access to health insurance does not help lower the risk of developing certain disease. In a study on thyroid cancer, multivariate spatial regression analysis showed socioeconomic and environmental factors, rather than access to insurance or health care facilities, are more likely to drive that disease.[4] For auto insurance, spatial factors rather than individual driving factors appear to drive insurance rates. Factors such as race and socioeconomic background, rather than consideration of driving behavior, were shown to demonstrate rates given.[5]

Simulated 1993 historical flood event at the vicinity of Jiroft city in southeastern Iran. From: Sarhadi, Soltani, and Modarres, 2012.

Simulated 1993 historical flood event at the vicinity of Jiroft city in southeastern Iran. From: Sarhadi, Soltani, and Modarres, 2012.

References

[1] For more on arcSlopeStab used for erosion and landslide modeling, see: Sitányiová, Dana, Terezie Vondráčková, Ondrej Stopka, Markéta Myslivečková, and Juraj Muzik. 2015. “GIS Based Methodology for the Geotechnical Evaluation of Landslide Areas.” Procedia Earth and Planetary Science 15: 389–94.

[2] For more information on flood risk mapping, see: Sarhadi, Ali, Saeed Soltani, and Reza Modarres. 2012. “Probabilistic Flood Inundation Mapping of Ungauged Rivers: Linking GIS Techniques and Frequency Analysis.” Journal of Hydrology 458-459 (August): 68–86. doi:10.1016/j.jhydrol.2012.06.039.

[3] For more on the use of EHRs for finding uninsured patients and spatial patterns, see:  Angier, H., S. Likumahuwa, S. Finnegan, T. Vakarcs, C. Nelson, A. Bazemore, M. Carrozza, and J. E. DeVoe. 2014. “Using Geographic Information Systems (GIS) to Identify Communities in Need of Health Insurance Outreach: An OCHIN Practice-Based Research Network (PBRN) Report.” The Journal of the American Board of Family Medicine 27 (6): 804–10. doi:10.3122/jabfm.2014.06.140029.

[4] For more on insurance and its relation to thyroid disease, see: Francis, Gary L., Steven G. Waguespack, Andrew J. Bauer, Peter Angelos, Salvatore Benvenga, Janete M. Cerutti, Catherine A. Dinauer, et al. 2015. “Management Guidelines for Children with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Task Force on Pediatric Thyroid Cancer.” Thyroid 25 (7): 716–59.

[5] For more on how auto rates have spatial characteristics, see: Ong, Paul M., and Michael A. Stoll. 2007. “Redlining or Risk? A Spatial Analysis of Auto Insurance Rates in Los Angeles.” Journal of Policy Analysis and Management 26 (4): 811–30. doi:10.1002/pam.20287.

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