Insurance risk has never been purely about who a driver is. It is equally about when, where, and how often a vehicle is actually used. Yet most motor insurance models still rely heavily on static proxies that struggle to capture this dimension.
Exposure to Risk (ETR) addresses this blind spot. As part of Dolphin’s MOVE Score, ETR adds a practical, data-driven view on real-world vehicle usage – without requiring insurers to run full telematics programs or handle raw mobility data.
Rather than replacing actuarial models, ETR complements them with a missing layer of context: actual exposure.
Why exposure matters more than demographics
Traditional underwriting variables such as age, postcode, or vehicle type remain useful, but they are indirect indicators. Two drivers with identical profiles can have vastly different risk simply because they use their cars differently.
Exposure to Risk focuses on this difference by separating driving quality from cumulative exposure. It looks at how much a vehicle is used, under what conditions, and at what times – capturing patterns that materially influence accident probability.
A simple thought experiment illustrates why exposure matters so much: imagine a highly disciplined, rule-compliant driver who takes 1,000 trips per year, mostly in dense urban traffic. Compare this with a clearly riskier driver who only drives once a year. In traditional models, the frequent driver may still appear to be the “better” customer based on demographics and assumed behavior. From an exposure-based perspective, however, the opposite can be true. The second driver is exposed to risk only once, while the first is exposed hundreds or thousands of times.
Examples of exposure-related risk factors include:
- Total mileage and frequency of trips
- Short trips versus long journeys
- Time of day and daily driving routines
- Road types such as urban, rural, or highway environments
These factors are strongly correlated with claims risk, yet they are largely invisible in traditional rating models.
How ETR works within MOVE Score
Within MOVE Score, Exposure to Risk is one of two complementary components, alongside driving behavior (MOVE IQ). While MOVE IQ focuses on how a vehicle is driven, ETR answers a different question: how much risk the vehicle is exposed to in the first place.
ETR aggregates usage-related signals such as mileage, trip duration, and road context into a single, standardized exposure metric. This allows insurers to compare vehicles on a like-for-like basis, independent of individual driver identities.
What insurers gain from exposure-based risk insight
Adding exposure-based risk intelligence enables insurers to sharpen decisions in areas where margins are tight and averages are no longer sufficient.
Typical use cases include:
- Identifying lower-risk vehicles within traditionally high-risk segments
- Detecting hidden exposure risks in portfolios that appear stable on paper
- Improving pricing precision without increasing model complexity
- Supporting fairer, usage-aligned risk segmentation
Because ETR reflects real-world usage rather than assumptions, it helps insurers move from proxy-based risk estimation toward evidence-based differentiation.
From measuring exposure to influencing risk
Exposure to Risk is not only an underwriting input. At its core, it captures the sheer volume of risk taken – how often a vehicle is on the road and how many kilometers it actually travels.
This matters because accident probability accumulates with every trip and every kilometer. Even excellent driving behavior cannot offset constant exposure. By making trip frequency and mileage explicit, ETR allows insurers to distinguish between drivers who are rarely exposed to risk and those who face it thousands of times per year.
When combined with prevention and engagement capabilities, these exposure insights can also be used to influence risk. Because accidents tend to cluster around specific situations – such as very short trips or certain road types – insurers can apply targeted communication, education, or incentives. In this way, the same exposure signal that improves pricing precision can also support safer mobility, without changing core models or launching new consumer programs.
