From Postcodes to Behaviour: Understanding the Geography of Risk

Understanding Risk Through Geography 

Geography has long been a core input in motor pricing, but in modern insurance, it’s no longer just a coordinate. Geography is a behavioural signal. It captures how people live, move, and interact with their local environment. 

At Consumer Intelligence, we combine our rich, market-representative dataset with powerful machine learning models through our Atlas and Apollo propositions. This combination unlocks insights that go beyond traditional rating or market benchmarking. 

• Atlas explains why geography matters, revealing the environmental, demographic, and behavioural context behind risk.
• Apollo shows how the market prices those same signals, across the full spectrum of propositions in the UK motor insurance market . 

Together, they provide a full-market lens into pricing, allowing us to see not only what different insurers are doing, but also how the entire market interprets geography. This means we can paint the complete picture - connecting local behaviour, pricing trends, and real-world outcomes in a single analytical framework. 

Penalty Points: Mapping the Behavioural Gradient 

Average Penalty Points per Licence’ remains one of the more direct and transparent measures of driving behaviour by geography. Across the market on average, we see a clear, rising relationship between penalty point prevalence and relative price. 

Nearly 90% of insurers displayed noticeable variations in price by this factor. Yet within that consensus lies variation: different insurers apply different thresholds and sensitivities. Some brands introduce uplift early and steeply, while others delay rate adjustments until higher thresholds are reached, or cap their curves entirely. 

This diversity creates both risk and opportunity. Depending on where your pricing curve sits relative to the market or subsets of the market, there may be pockets of overexposure or underexposure , areas where your premiums are more competitive than intended, or conversely, where they price you out unnecessarily. 

That’s where the value of Atlas and Apollo comes in. Together, they allow us to provide insurers data-driven, specific and actionable pricing recommendations that drive real value and are followed-through all the way to implementation. 

Bus Commuters: When Mobility Patterns Shape Risk 

‘The proportion of bus commuters’ is a more polarising feature. Whilst many insurers do apply meaningful uplifts in price as bus reliance increases, others rate very lightly or indirectly , or not at all. 

Our motor pricing analysis shows that the market average curve rises fairly modestly with higher bus use, but the spread between brands is significant. This inconsistency reveals that not all insurers interpret the risk behind transport composition in the same way, and it’s precisely this variation that makes benchmarking so valuable. 

For insurers not currently rating on this factor, Apollo shows how many competitors are, and to what extent. This helps identify whether a missed signal represents untapped segmentation potential. For those already using it, Apollo reveals how their pricing curve compares with the wider market and pinpoints areas of possible over- or under-exposure. Postcodes with higher bus commuter rates often share characteristics such as: 

• Denser urban environments with higher traffic density and easier accessibility to services.
• Lower household vehicle ownership and shorter travel distances.
• Broader socio-economic diversity influencing driving patterns and claim propensity. 

From an exposure perspective, regions with heavy public transport usage can also experience higher frequencies of pedestrian and cyclist collisions with cars, particularly near bus corridors and city centres. While quite infrequent, they may result in disproportionately high claim severity due to bodily injury and associated medical costs.  

Notably, the commuter dataset is among those where more recent data shows weaker correlations to claim performance, a legacy of pandemic-era disruptions to commuting patterns.  

Together, Apollo and Atlas help turn this complexity into clarity and offering insights into why , linking transport behaviours to real-world exposure, affluence, and accessibility. This combination allows insurers to see not only whether the signal exists, but how its interpretation varies across the market, and where competitive opportunity may lie. 

Armed Forces Veterans: Balancing Risk, Behaviour, and Fairness 

The proportion of armed forces veterans in an area is a particularly interesting and surprisingly relevant geographical factor (at least to non-actuaries), it is one that blends behavioural data with deeper questions of fairness and perception. 

From an actuarial standpoint, veterans as a group tend to exhibit safer-than-average driving behaviour. This may be due to more disciplined adherence to road rules, traits likely shaped by heightened risk awareness, and situational discipline. This makes the signal not just statistically interesting, but behaviourally intuitive. 

Across the market, our Apollo analysis shows that leading insurers do in fact recognise and reward this signal through meaningful price reductions. From areas with few or no veterans to those with higher veteran prevalence, we observe average premium discounts ranging from 2% to 10% from many competitive providers. 

While the scale of the adjustment varies, the direction is consistent, a downward rating trend, reflecting lower perceived risk.  

Yet this is also one of the few features where pricing ethics and public perception intersect directly with data-driven decision-making. Applying a discount is commercially uncontroversial, but introducing a load would be viewed very differently. Even if the data were to show higher loss ratios in certain contexts, increasing prices for veterans would likely raise reputational and maybe even regulatory concerns. 

Atlas; our postcode rating proposition helps by revealing the geographic and socio-economic contours of veteran concentration, often correlated with proximity to bases, rural communities, or historically military towns. Apollo then quantifies how each insurer responds, highlighting how many insurers are using the factor and to what degree. 

Every insurer has their own claims datasets and is comfortable pricing within the confines of their own experience. However, expanding into segments with limited prior exposure and claims data can be both challenging and risky. As competition intensifies and rate differentials narrow, targeted and segmented pricing is becoming increasingly important, allowing insurers to optimise performance and find growth in parts of the market beyond the ‘vanilla-verse’ where nearly everyone competes.  

In a market where transparency and precision are paramount, understanding how and why geography drives pricing can make the difference between precision and noise. With Atlas and Apollo, insurers gain both sides of the equation, the behavioural context behind risk, and the full-market view of how it’s being priced. Together, they turn postcode-level pricing data into strategic intelligence, helping pricing, underwriting, and distribution teams make decisions that are not just informed, but explainable.