Insurers face more pressures than ever before. Catastrophe losses are rising, fraud is becoming more sophisticated and the industry is changing fast. Traditional risk models, built on broad variables like zip codes or census boundaries, can't keep up with the complexities of reality. This is where location context comes in. With highly accurate geospatial and traffic data, insurers can underwrite with precision, offer fairer pricing and streamline claims — protecting both customers and profitability.
Mapping property risk with more precision
When it comes to pricing property insurance, location is everything, but it goes far beyond the obvious factors like the city or neighborhood. Insurers need to understand potential risk in much more granular detail, such as what floor of a building a property is on or where the building lies on a parcel of land. A third-floor apartment might face very different risks than one on the first floor, especially in the event of a fire or theft. Another factor that would make a difference is the property's proximity to important emergency services.
"How close is the property to the nearest fire station? How quickly can emergency services respond? Are there enough fire hydrants nearby?"
"These are the kinds of questions that should influence how premiums are calculated, but this level of detail is often missing from the data insurers have had until now."
With precise data on points of interest (POIs) like fire stations, forests and even proximity to disaster-prone zones, insurers can learn a lot about potential risks. If you live near a wildfire-prone area in California, for example, that should factor into your policy pricing. Without such intricate, contextual information, insurers end up putting themselves at financial risk by undercharging or driving away customers by overcharging. With the right location intelligence, insurers can strike the right balance and build smarter, fairer property insurance products.
Building better auto insurance models
Traditionally, insurance pricing has relied on generalized models to inform policies. Insurers would assess risk using zip codes or census boundaries, creating a one-size-fits-all approach that would often fail to capture the nuances of individual locations and behaviors. A driver in one part of a zip code might be significantly safer than another but would have the same risk profile.
"Until recently, auto insurance risk assessment models didn't take into account real-world variables. Every day, traffic patterns change, construction patterns change, traffic volumes shift. But the risk models remained static."Poomalai explains.
The inaccuracies caused by this approach didn't just affect the rates for customers, it also impacted the insurance company's underwriting profitability, says Poomalai.
These problems can be avoided by incorporating detailed map and traffic data into risk assessment models — traffic volumes, road conditions, the slope or curvature of roads and how busy it gets at different times of day. By looking at the full picture, insurers can assess the risk of a road or area much more accurately and offer coverage that actually reflects reality on the ground.
But it's not just about where an accident happens — it's also about who's driving and how. That's where location data adds the crucial context that insurers need. Say someone brakes hard. That could mean two very different things depending on where it happens. Doing it in a parking lot is one thing, doing it at a high-speed intersection that should be approached with caution is another. Why the driver did so is also an important factor. Did they brake hard because of an accident directly ahead, or because of roadworks?
By tying driver actions to additional location context, insurers can tell whether someone is regularly speeding in high-risk areas, making illegal turns or running one-ways, and then use that information to create more accurate, personalized risk profiles. In this way, good drivers reap the benefits through more accurate, fair pricing, while those who display riskier behaviors get clear incentives for becoming better drivers.
Verifying claims with context
Location data acts as an important tool for insurers to not just improve risk assessments, but also to validate claims and spot potential fraud. For example, if someone reports damage to their car due to a storm, insurers can quickly verify if that storm occurred at the reported time and place and if it was actually that severe.
This contextual insight helps insurers move faster on legitimate claims, making things smoother for honest customers, while also flagging suspicious patterns that suggest fraud.
For insurers, reinsurers and actuarial teams, location intelligence is no longer optional. It's the foundation of profitable, competitive insurance models. From sharper underwriting and portfolio risk management to faster claims processing and fraud detection, TomTom's data helps insurers build sustainable, competitive business models in an increasingly more complex world. Those who embrace it gain an edge: fairer pricing for customers and stronger profitability for their business.
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