The insurance coverage pricing world is evolving quick. We’ve moved from manually engineered GLMs to machine studying fashions that may seize intricate non-linearities and adapt to complicated market behaviours. These fashions are highly effective, however they’re additionally tougher to interpret.
And that’s the place the true problem begins.
Accuracy alone isn’t sufficient. A mannequin is barely as helpful as your means to elucidate it. And extra importantly—perceive what to do with it. That’s the place explainable AI (XAI) steps in. Achieved effectively, it provides pricing professionals the power to problem, calibrate and talk their fashions with confidence.
However XAI isn’t only a instrument for governance or validation. It’s a lens. A means of seeing issues in another way. And when paired with wealthy knowledge and the proper area experience, it will possibly reveal strategic alternatives hiding in plain sight.
Extra Than Marginal Beneficial properties
At Client Intelligence, we use a spread of XAI instruments like SHAP, HSTATS and partial or 2-way dependence plots to interrogate the behaviour of our proprietary pricing engine, Apollo. These instruments assist us perceive not simply which options drive predictions, however how these options work together—and whether or not the mannequin is responding to real patterns or simply noise.
However these instruments don’t give us solutions on their very own. They’re strongest when used alongside pricing experience and area context—particularly when supported by wealthy characteristic knowledge that helps clarify why a sign exists, not simply the place it exists.
That is the place our postcode enrichment layer, Atlas, comes into play.
Making Sense of Threat with Atlas
Atlas is our geospatial knowledge engine—constructed to explain the setting round every UK postcode utilizing over 200 engineered options. These embody public datasets from the Workplace for Nationwide Statistics, Division for Transport and Met Workplace, alongside proprietary engineered measures.
These options span areas akin to transport patterns, environmental stress, street community accessibility, and contextual indicators of visitors collisions. Whereas some variables—like commuting modes or native financial circumstances—derive from Census sources, others seize extra exterior, structural circumstances that affect how and the place threat emerges.
Importantly, Atlas doesn’t try to infer causality straight. However when utilized in mixture with characteristic outputs from machine studying fashions, it turns into a strong lens to discover and refine hypotheses about what is perhaps driving sure pricing behaviours or efficiency patterns.
For instance, deprivation indices—summarised from numerous underlying measures—are a well-known part in pricing. However when you possibly can isolate and check particular subcomponents like long-term unemployment, academic attainment, or transport availability, you possibly can higher perceive the seemingly causes of elevated threat particularly areas. And that offers pricing groups clearer choices for refinement, segmentation or messaging—not simply ranking.
Equally, Atlas contains airport proximity options. Collision knowledge from the Division for Transport exhibits that the realm surrounding main airports might be considerably riskier than the nationwide common. Impartial evaluation by Angelica Options confirmed that injury-causing collisions close to Heathrow have been over twice as frequent per capita than elsewhere. Whereas this type of spatial correlation is fascinating in itself, it turns into much more highly effective when explored within the context of modelled uplift. It opens up discussions round potential causes—like driver fatigue, unfamiliar autos, or elevated congestion—and tips on how to handle or mitigate them.
This sort of pondering isn’t about explaining the mannequin for the sake of it. It’s about bringing collectively mannequin output, real-world context, and pricing experience to know what’s actually occurring—and what might be finished about it.
Why This Issues
Correlation is the spine of a lot of insurance coverage pricing. However once we can start to know the trigger, we are able to do extra. Not simply construct higher pricing, however assist form safer behaviours, fairer outcomes and extra knowledgeable conversations throughout the enterprise.
Explainable AI instruments assist pricing groups do greater than spot uplift. They assist them make sense of it. They flip opaque outputs into comprehensible logic. And when used with geospatial enrichments like Atlas and examined in opposition to ranking components like age, NCD, mileage or occupation, they reveal relationships that may reshape how threat is seen—not simply inside pricing, however throughout underwriting, advertising and past.
And that’s the true alternative right here. It’s not nearly defending a mannequin. It’s about informing the organisation. Serving to each stakeholder, from analyst to underwriter to government, perceive what issues and why. So we are able to worth with confidence, adapt with agility, and transfer from reactive modelling to proactive technique.
As a result of richer alerts aren’t the top purpose. It’s what we do with them that counts.
