Bringing Precision to Risk Assessment and Fraud Detection with AI-Powered Location Intelligence

It worked—because it had to. But today, those models are starting to show their limits. For many carriers, it's not failing loudly. It's quietly eroding accuracy—policy by policy, quarter by quarter.

At the same time, the industry is accelerating toward AI-driven underwriting, claims automation, and fraud detection. But one reality is becoming increasingly clear across P&C: AI is only as reliable as the data that grounds it. Or, put simply, grounding data makes or breaks AI agents in production.

In insurance, that's not just a technical concern. It's a business one.

The mismatch between modern risk and outdated models

Property and casualty insurers are operating in a more volatile environment than ever before. Traffic patterns are evolving, infrastructure is changing, and exposure to risk—whether driven by behavior or environment—is becoming more dynamic. And yet, many models still rely on simplified geographic proxies.

The assumption that a zip code represents a consistent level of risk was always an approximation. Today, it is increasingly inaccurate.
Two addresses in the same zip code can sit on roads with completely different accident rates. Driving behavior varies by corridor, not postcode, and risk changes continuously—by time of day, by season, and as infrastructure evolves—while static models remain fixed.

These are not edge cases. They are everyday realities.

When these differences are averaged out, insurers aren't just simplifying risk—they're distorting it.

AI raises the stakes but doesn't fix the problem

The industry's move toward AI is both necessary and inevitable. Underwriting, fraud detection, and claims processing are all being enhanced with AI, ML, and automation.

But there's a misconception that AI will solve the problem of imprecise data. It doesn't.

AI amplifies the quality of the data it's trained on. If that data is incomplete or outdated, the outputs will reflect those same gaps—just at scale.

In underwriting, that shows up as mispriced risk. In claims, it leads to decisions based on incomplete context. In some cases, it results in hallucinations—outputs that seem coherent but don't align with reality.

For insurers, those aren't abstract risks. They translate into coverage written on a stale view of the world or claims decisions made without sufficient validation.

That's why grounding AI in accurate, real-world data is becoming essential.

Moving from boundaries to behavior in risk pricing

If insurers want to price risk more accurately, it's worth asking a simple question: what are we actually measuring?

Traditional territory models group risk by administrative boundaries. But risk doesn't behave that way. It's shaped by the roads people actually use, the traffic patterns they navigate, the design of intersections and road networks, and the way mobility changes over time.

AI-powered location intelligence makes it possible to model these dynamics directly.

Instead of relying on broad geographic zones, insurers can define territory risk based on how environments behave in the real world. Granular data—such as address-level insights, road attributes, and traffic patterns—allows insurers to move from approximation to precision.

Solutions like TomTom's Orbis Maps bring these layers together into structured datasets that can be integrated into actuarial models. The shift may seem subtle, but its impact is significant: from estimating risk to measuring it with far greater clarity.

Fraud and claims: a growing pressure point

In my recent conversations with leaders across auto insurance carriers, one priority keeps coming up: improving fraud detection and claims validation.

That's not surprising. Industry estimates suggest that 10 to 15 percent of all P&C claims involve fraud, with auto particularly exposed. But what stands out in these discussions is that fraud doesn't just erode loss ratios—it creates a cascade of downstream challenges.

Claims validation becomes slower and more complex. Operational costs increase. Customer experience suffers. And ultimately, premiums rise to compensate—penalizing the very customers insurers are trying to serve fairly. Breaking that cycle is becoming a strategic priority.

Where location intelligence is making a difference

Two use cases consistently come up in these conversations.

The first is First Notice of Loss (FNOL) verification, which is often the most critical moment in the claims lifecycle. The ability to validate incident details quickly and accurately at FNOL can materially change both loss outcomes and operational efficiency.

The second is garaging fraud detection. When policyholders misrepresent where a vehicle is primarily kept, it undermines underwriting discipline and drives unfair pricing. It's also notoriously difficult to detect without precise location data.

To address these challenges, carriers are investing heavily in AI-driven claims automation. But again, success depends on the quality of the underlying data.

What is often missing is granular, verifiable location context.

Location intelligence begins to fill that gap by introducing several critical layers of insight. Reverse geocoding allows insurers to translate GPS data into precise addresses, helping validate FNOL reports and reveal inconsistencies. Historical traffic and incident data provide a way to check whether reported events align with real-world conditions at a given time. Hazard data helps confirm whether environmental or road conditions could have contributed to an incident. And road context—such as speed limits, curves, and intersections—makes it possible to assess whether a claim is plausible.

Together, these layers enable insurers to move beyond reactive validation toward more proactive, data-driven decision-making.

The rise of claims hyper-automation

What's emerging is a broader shift toward claims hyper-automation, where AI, automation, and location intelligence converge.

By embedding location data directly into claims workflows, insurers can accelerate resolution times, reduce loss adjustment expenses, detect fraud earlier, and improve customer satisfaction.

This isn't a future concept. It's already being implemented by carriers looking to operate more efficiently and compete more effectively.

The shift toward location intelligence is already underway. Some insurers and ecosystem partners are integrating high-resolution, real-world data into underwriting and claims today. Others are continuing to rely on traditional proxies. The result is a growing gap in decision quality.

Insurers working with richer location intelligence are pricing risk more accurately than those who are not. And that advantage compounds over time, showing up in more stable portfolios, improved loss ratios, and more efficient operations.

Carriers who act now will have a measurable head start over those who wait.

The bottom line

Insurance has always been about understanding risk. What's changed is the level of precision required to do it well.

Zip codes were once a practical shortcut. Today, they are an increasingly blunt instrument in a much more complex environment.

AI-powered location intelligence offers a way forward—grounding underwriting and claims decisions in how risk actually behaves in the real world.

For P&C insurers, the opportunity is clear.

The organizations that embrace this shift won't just improve accuracy. They'll fundamentally reshape how risk is priced, validated, and managed.

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