In the insurance industry, geospatial data is one of the essential information types. Underwriters use it to assess risk and react to potential issues related to the insured clients, as well as their property. Today, insurance teams can reduce expenses significantly thanks to new technologies and overall better data processing approaches. When it comes to location data, geographic information systems (GIS) can help insurers a lot.
Below, we will look at GIS in insurance, usage of spatial data, general benefits, and use cases. Read on to find a viable development direction for your insurance business!
Geospatial Information and Insurance
Basically, underwriters deliver policies to customers who then can get compensation in case of any problem. Evidently, both parties aren’t interested in activating their policies. For insurers, this means extra expenses related to paid claims. For users, this leads to losses only partially covered by claims.
Thus, it’s mutually beneficial when an insurance agency can predict risks and help clients to avoid them. As for location-sensitive policies, they include property insurance for homes and cars, as well as life insurance that protects clients from various emergencies. Here, GIS helps in protecting from natural disasters, crimes, accidents, etc.
Let’s look at how exactly GIS and spatial information can be used.
Types of Spatial Data
Overall, there are a lot of valuable insights obtained through spatial analysis. Experts in digital geo systems work in various industries where locations matter. In the insurance sector, we can distinguish five key data types that software for insurance agency should be capable to process:
- Existing policies. Show who and where has policies, what is protected, and for which sum. This data has to understand potential risks better. Insurers can spot areas of greater danger and understand how to treat these zones.
- Firmographics. This type shows which businesses are located side by side. For example, a bookstore near a fire station will feature fewer risks than the same store but situated in front of a pyrotechnics shop.
- Historical loss information. This global category includes all losses for the entire observation period. Thanks to historical analysis, it helps to find out which areas suffered from more significant damage and which ones are more protected.
- Location risk index. For businesses and regular customers, each location features a specific risk index. For business environments, the parameter considers economic situation while individuals should remember about criminal landscape, for instance.
- Natural disasters. One of the most famous examples of using geospatial data in insurance. It shows how vulnerable are properties/people to different hazards: fires, floods, tornadoes, earthquakes, etc.
Insurance Processes Involved
Moving to the general usage of GIS and GIS-related information in the insurance business, we want to focus on one key idea. The main advantage of modern spatial analysis is that advanced digital tools support real-time data collection and analysis. That’s why insurers can predict risks and react to them much quicker. Below, you can see a few processes that benefit from real-time GIS integration:
- Claims management. Modern geo tools facilitate understanding of customer locations so companies can respond to requests way better. It’s a next-gen client-focused approach that prioritizes the satisfaction of insured persons or businesses.
- Risk assessment. As we’ve mentioned above, GIS, combined with other analytical systems, can predict risks way more precise. This results in optimized premiums for different areas, as well as better preventive services.
- Underwriting. Respectively, GIS also can boost underwriting processes. With spatial data, agencies understand who should be insured, how to target clients, which premiums and policy types to deliver, and so on.
The last part that relates to all of the aforementioned processes is fraud detection. Even today, some clients try to cheat on agencies to get the money without facing any damage. Geospatial data helps to understand fraudulent behavior better thanks to historical analysis and real-time comparison with legal claims.
Some Real-World Use Cases
This last section includes a few notable examples of how GIS power can be used in insurance. Particularly, it’s based on insights unveiled by developers, analytical companies, and even governmental projects.
1. Behavior-Based Car Insurance
The combination of GIS data and smart IoT sensors boost car insurance processes. Particularly, geospatial data from GPS with telematics from smart devices result in 360-degree driver portraits. Users and insurers see driving speed, braking habits, cornering, acceleration, and so on. Thus, underwriters can categorize their customers by risk levels and deliver more personalized policies for both cars and drivers.
2. Protection from Fraudulent Farmers
The problems of global climate change are well-known. Farmers around the world face new challenges, so they tend to get their fields insured more often. But some businesses want to speculate on the current situation. To protect from fraudulent behavior, companies can use remote sensing data from satellites like Landsat. The most popular approach is to analyze crop health by using NDVI – the index clearly shows which fields are in danger.
3. Analysis of Natural Disasters
Of course, it’s one of the most common use cases. Insurers cooperate with national teams to analyze geospatial data and find areas with a high level of risk. For instance, GIS can run overlay analysis to identify zones of forest fires, wind directions, and regions with a large number of policyholders. As a result, insurers get maps based on several factors that reveal combined risks. ESRI has a great visual explanation of this point.
4. Radar Modeling for Flooded Areas
Here, historical analysis helps, too. GIS specialists work with underwriters to identify flood-prone zones, see how often floods are, and how much damage they cause. Mostly, this is done via radar and hydrological data, but it’s possible to run a similar analysis for earthquakes, fires, and even social problems. The benefits are clear: based on the results of analytics, insurers can tune premium size for each area separately.
Further Development of GIS
It seems that spatial data will remain highly essential for insurers. With further development of AI analytical platforms, data processing solutions, and automated insurance software, we will see more and more successful risk prediction. Ideally, the industry can protect customers around the globe from significant losses related to natural disasters and other hazards.
