Some of the brightest minds in actuarial modeling, data science, and machine learning work in insurance. That’s because insurance is deeply dependent on using measurable truths about the world (data) to predict the future. Better predictions about the future lead to more accurate risk predictions. More accurate risk predictions mean more precisely written PIFs (policies in force). More precise PIFs means fewer high-risk exposures and more profitable policies, which means a more profitable insurance business. There is a reason self-proclaimed data-nerds have been working in insurance for 100s of years before “data science” was cool, and that’s because in the world of insurance, data is king.
To see the technical aspects of this blog put into action via code,
check out the accompanying Google CoLab Notebook
There are a lot of factors to consider when insuring a commercial business, and many of those are geospatial. If you are selling flood insurance, for example, knowing whether a business is located near a river with a history of flooding as well as how likely that river is to flood again is critical to writing a risk-balanced policy. Similarly, if you are selling fire insurance, you need to know if a business shares a wall with an open-flame commercial kitchen and bakery.
But where does this relevant geospatial data come from?
Sharing a wall with an open-flame commercial kitchen is a great example of co-tenancy risk. Unfortunately, co-tenancy risk is particularly difficult to assess. Unlike primary data about the business (like where is the business located), the policy-holder may not be fully aware of the relevant co-tenancy information (like what are the surrounding businesses). Most natural disaster-related geographic data—such as knowing whether a given business is located in a flood zone and how much it rains at this location—is collected by governments, changes infrequently, and is available via many GIS solutions. In contrast, point-of-interest (POI) co-tenancy data changes frequently as businesses open and close or as malls and retail parks are rebuilt or expanded. Accurate, complete, and timely POI co-tenancy data is rarely available in existing GIS solutions.
Imagine that you need to assess the co-tenancy fire risk for a business. Assuming you have access to accurate POI co-tenancy data, the first thing you need to verify is whether the insured business is near another business that has a high risk of fire. This requires knowing:
SafeGraph has the most accurate and comprehensive category and roof-top geocoding data for commercial businesses—just ask Verizon, Esri, and Mapbox. Check it out for yourself!
But roof-top geocodes are not enough. Being across the street from a high-risk POI does not carry the same risk as sharing a wall or being in the same building,even if the distances between roof-top geocodes are the same.
Co-tenancy data is, therefore, about understanding the geospatial relationships and hierarchies between businesses. Is this business located inside of another business, such as a Starbucks inside a Kroger grocery store? Is this business within its own stand alone building or does it share a parent structure (parent building) with other businesses, as is typically the case for indoor or outdoor malls? The risk of a fire spreading from one business to another is much greater when businesses share a wall or a building.
SafeGraph Places is the most comprehensive and accurate dataset about commercial points-of-interest, covering 6MM+ POI in the United States and Canada.
SafeGraph Places was built as a geospatial POI dataset from the very start. It goes beyond simply providing essential metadata (i.e. address, category, corporate branding, etc.) to give you access to rich geospatial data, including precise roof-top geocodes, polygon building footprints (2-D shapes), as well as co-tenancy information, such as business and building "parent-child" relationships (i.e. Is this POI inside another POI and/or does this building structure contain multiple businesses?).
The key fields related to co-tenancy data are the parent_safegraph_place_id and the polygon_class columns.
Let’s apply this to the real world and look at how you can use this data effectively to assess the co-tenancy fire risk for a chain of fitness centers in California.
Let’s consider the portfolio of Anytime Fitness (SG_BRAND_6daa255524fe5ac244c3bed9cfbde479) locations in California. At the time of first publication (February 2020), Anytime Fitness had 124 locations across California.
Now, imagine we are tasked with underwriting a commercial insurance policy for these 124 business locations, with the risk of fire damage as a key consideration factor for this policy.
It’s important to note that the risk of any business sustaining fire damage is low (fires are rare), and, moreover, not all POI are equally at risk of fire damage. A clothing store is much less likely to experience an accidental fire than a commercial kitchen or bakery, because the latter have open flames and hot ovens burning all day every day. We need to know what other businesses are in close proximity to each Anytime Fitness location in order to accurately assess the risk of fire damage.
Similarly, as a mitigating factor to minimize the potential risk of fire damage, we want to know how closely located our locations are to fire stations. After all, in the unfortunate event of a fire, the number of seconds and minutes until a fire truck arrives has a significant impact on how much fire damage is incurred. Therefore, locations that are closer to fire stations have less risk of fire damage than locations located far away from fire stations.
To see this insurance case study example fully implemented in Python, check out the accompanying Google CoLab Notebook.
For our model, we defined high-risk POI for fires as any POI belonging to the following NAICS:
Our simple fire risk model consists of three components:
We combined these features into a simple model to calculate a risk score:
To see the full results and play with the code,
check out the the accompanying Google CoLab Notebook.
Remember, this is only a simple model for assessing fire risk. In practice, a risk model may account for many other factors, including building materials, weather data, appliance data, foot-traffic data (SafeGraph Patterns), etc. The weight given to each of these variables can be determined by fitting a model on many years of historical claims data.
Nonetheless, despite its simplicity, our model reveals interesting insights about fire risk when applied to the Anytime Fitness locations in California.
Figure 1 shows a surprisingly symmetric histogram of risk scores for the 124 Anytime Fitness locations. Most locations are clustered around a neutral risk score of 0. However, a handful of Anytime Fitness locations are on the far left tail of this distribution, which means these locations have the lowest risk for potential fire damage. Let’s take a closer look at an example of what this means in a real world scenario.
Figure 2 shows an Anytime Fitness with one of the lowest risk scores (-134) in California . What makes it so low risk? First, it has zero same-building co-tenants,which also means it has zero high-risk co-tenants. Second, it’s not near any high-risk POI; it shares a parking lot with a dentist office and a child-care center—both of which are low-risk POI for fire). The nearest high-risk POI is a restaurant located about 72 meters away and across the street. Being that far from a high-risk POI is unusual for Anytime Fitness locations (median = 47 meters). Finally, the red-arrow indicates the nearest fire station, which is very close. Therefore, considering its close proximity to a fire station and the absence of nearby or co-tenant high-risk POI, our model identified this as a very low risk location.
What about the other end of the spectrum? Figure 3 shows the Anytime Fitness location with the highest risk score (+153) in California. This is because it’s located within a strip mall that contains six other high-risk POI for fire. These are all considered co-tenants because the strip mall is one contiguous structure (in the SafeGraph schema, they all share the same parent_safegraph_place_id). To throw gas on the fire (pun intended), the nearest fire station is 2,800 meters away, much farther than the average distance (median = 1,124 meters).
Want to examine other high or low risk locations?
Want to replicate for your own locations? See the entire example fully implemented in the accompanying Google CoLab Notebook.
This article demonstrates how SafeGraph Places point-of-interest (POI) data makes it possible to build more accurate commercial insurance risk models. Our simple model of fire risk requires the following:
Traditionally, it has been hard to find accurate data on these attributes for all places. We are changing that at SafeGraph. Our number one focus is to make this data as complete, accurate, and accessible as possible, helping you build better models and more accurate predictions.
Risk modeling is complex and has many variables to consider. Here are some other ideas for how SafeGraph data can enhance your risk-modeling:
Want to try this model out on more locations? Get started with $200 worth of data for free at the SafeGraph Data Bar when you use code: DeRiskWithData.
Then plug your data into the accompanying Google CoLab Notebook to replicate the results of this blog post.
Ideas, feedback, bug discoveries, or suggestions? Send to [email protected]
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