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 actual problem begins.
Accuracy alone isn’t sufficient. A mannequin is simply as helpful as your means to clarify it. And extra importantly—perceive what to do with it. That’s the place explainable AI (XAI) steps in. Performed properly, it offers pricing professionals the flexibility to problem, calibrate and talk their fashions with confidence.
However XAI isn’t only a software for governance or validation. It’s a lens. A manner of seeing issues in a different way. And when paired with wealthy knowledge and the appropriate area experience, it will probably reveal strategic alternatives hiding in plain sight.
Extra Than Marginal Positive aspects
At Client Intelligence, we use a variety 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 function 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 Danger with Atlas
Atlas is our geospatial knowledge engine—constructed to explain the atmosphere 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 equivalent 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 and infer causality straight. However when utilized in mixture with function outputs from machine studying fashions, it turns into a robust lens to discover and refine hypotheses about what is likely to be driving sure pricing behaviours or efficiency patterns.
For instance, deprivation indices—summarised from numerous underlying measures—are a well-recognized part in pricing. However when you may isolate and check particular subcomponents like long-term unemployment, instructional attainment, or transport availability, you may higher perceive the doubtless causes of elevated threat particularly areas. And that provides 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 world surrounding main airports could be considerably riskier than the nationwide common. Unbiased evaluation by Angelica Options confirmed that injury-causing collisions close to Heathrow had been over twice as widespread per capita than elsewhere. Whereas this type of spatial correlation is attention-grabbing in itself, it turns into way more highly effective when explored within the context of modelled uplift. It opens up discussions round potential causes—like driver fatigue, unfamiliar automobiles, or elevated congestion—and learn how to handle or mitigate them.
This type 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 could 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 towards 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 actual 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 govt, 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.