The 2025 LexisNexis U.S. Auto Insurance Trends Report paints a clear, data-backed picture of the auto insurance landscape: severity is up, violations are spiking, and shopper behavior is more active than ever. But beyond the headlines, there’s a deeper shift taking place — one that traditional risk models may not be equipped to handle alone – auto insurance is getting riskier.
Let’s break it down.
The Numbers Tell a Complex Story
Here are some of the most telling trends from the LexisNexis report:
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Bodily injury claim severity rose 9.2% in 2024 — its highest level in five years.
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Driving violations jumped 17% year-over-year, with distracted driving up 50%, major speeding up 16%, and DUIs rising 8%.
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EV claims showed 14% higher frequency than those involving internal combustion engine vehicles.
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Quote shopping hit a record, with over 45% of auto insurance policies shopped at least once.
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Direct written premiums grew to $359 billion — a 13.6% increase over 2023.
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Rate increases are decelerating — just 10% in 2024 compared to 15% the year before.
In short, carriers face a market characterized by heightened risk, more consumer movement, and a softening yet volatile underwriting environment.
And while carriers are still incredibly profitable as their recent rate hikes have taken effect, there is an undercurrent of anxiety amidst rising claims costs and increased competition. With that, one question looms large: How do we price risk more accurately — without slowing down the customer journey?
At ForMotiv, we believe the answer isn’t just more data — it’s the right data at the right time.
⚠️ The Hidden Risk in the Application Process
While external trends like inflation and repair complexity contribute to rising costs, human behavior is at the core of many of these issues. More drivers are exhibiting riskier behaviors behind the wheel — and behind the keyboard.
Most of today’s pricing and underwriting models are powered by third-party data, historical loss trends, and self-reported answers. But as LexisNexis notes, shopping activity is climbing (we wrote more about this here) — and with it, the potential for quote manipulation, omitted details, and fraudulent behavior.
The problem? Traditional models don’t see how someone fills out an application — only what they say.
That’s where ForMotiv comes in.
At ForMotiv, we work with top carriers to analyze the digital body language of applicants and agents during the quote and application process. Our platform captures behavioral micro-expressions—things like hesitation, rapid field changes, erratic navigation, or copy/paste behavior — that help predict:
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Potential misrepresentation
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Quote manipulation
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Agent-driven gaming
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Risk intent before submission
In a landscape where loss ratios are tightening and fraud is becoming more sophisticated, these in-session insights offer a new dimension of underwriting and fraud detection that can be leveraged in real-time.
It’s Not About More Data — It’s About Better Timing
The challenge for carriers isn’t a lack of data — it’s a lack of visibility into the earliest moments of the customer journey. By the time a policy is priced and bound, the opportunity to intervene has often passed.
Behavioral data provides an earlier glimpse into applicant intent.
Used properly, it allows carriers to:
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Proactively triage high-risk applications
- Increase conversions of high-intent, low-risk shoppers
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Enhance underwriting decisioning without increasing friction
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Reduce downstream losses tied to misrepresentation or early lapse
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Identify agents or users exhibiting risky digital behaviors
The Takeaway
Insurance is built on understanding risk. But as consumer behaviors evolve, digital interactions dominate the application funnel, and auto insurance gets riskier, the way we evaluate that risk needs to evolve, too.
LexisNexis’s report highlights the challenges. Behavioral intelligence points to a solution — not by replacing traditional models, but by augmenting them with deeper, earlier insight.
Because in today’s environment, the best risk decisions don’t come from having more data — they come from having the right data at the right moment.
Interested in learning more? Let’s chat.
