Machine Learning and AI for Insurance Pricing
Transform your pricing strategy with explainable AI and geospatial intelligence. Harness the power of machine learning to drive strategic decisions and uncover new market opportunities across the insurance landscape.
AI for insurance pricing, you can explain and deploy
AI for Insurance Pricing at Consumer Intelligence helps UK pricing and insight teams move from reactive modelling to proactive strategy—using explainable machine learning grounded in real market data. Our approach combines XAI techniques (e.g., SHAP, PDPs) with geospatial postcode intelligence, so you understand the “why” behind price and risk, not just the score.
With Apollo for strategic pricing intelligence, Atlas for postcode classification, and Machine Learning Pricing Indices embedded in Daily Price Benchmarking, you can simulate scenarios, calibrate rating curves, and track factor movements with confidence, supported by real-time market signals and verified national datasets.
"Our solutions help you move beyond reactive modelling to proactive strategy, delivering competitive advantage in an increasingly sophisticated market"
Tim Stout, Head of Growth and Innovation
Move beyond reactive modelling to a proactive strategy
We don't just build accurate models, we build models you can understand, trust, and defend. Our explainable AI approach transforms complex machine learning outputs into actionable insights that drive strategic decision-making across your organisation.
From uncovering pricing inefficiencies to identifying underserved market segments, our solutions help you move beyond reactive modelling to proactive strategy, delivering competitive advantage in an increasingly sophisticated market.
Practical, explainable Machine Learning built on UK insurance market data.
Engineered Features
Years Real Daily Pricing Data
UK Postcodes Covered
Data Sources
Our Machine Learning Solutions
Beyond prediction: insight that drives strategy. Our explainable AI approach transforms complex machine learning outputs into actionable insights for strategic decision-making.
Apollo
Strategic Pricing Intelligence
Atlas
Postcode Classifier & Geospatial Risk Intelligence
ML Pricing Indices
Daily Price Benchmarking with Machine Learning
Video: The Case for Explainable AI
Accuracy alone isn’t enough. A model is only as useful as your ability to explain it. And more importantly, understand what to do with it. That’s where explainable AI (XAI) steps in. Done well, it gives pricing professionals the ability to challenge, calibrate and communicate their models with confidence.
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Methodology
Built for accuracy and explainability
Gradient Boosted Machine Learning
Over 250 carefully engineered features trained and tested via cross validation on unseen data, inclusive of our proprietary Postcode Classification model. Our GBM framework is specifically optimised for insurance pricing applications, balancing accuracy with explainability.
Explainable AI Tools
SHAP, HSTATS, partial dependence plots, and multi-factor analysis to uncover nuanced feature interactions and ensure transparent decision-making. These tools transform 'black box' models into clear, defensible insights that can be communicated across technical and commercial teams.
Real-Time Market Intelligence
Models built with 2+ years of real daily pricing data, benchmarked against aggregated market models for accuracy and reliability. This market-centric approach ensures our solutions deliver actionable competitive insights, not just theoretical improvements.
Verified Data Sources
Draws from verified national sources including ONS, DfT, Met Office, Police, and Census data, combined with 50+ proprietary datasets. Our rigorous data governance ensures all inputs meet the highest standards for accuracy, recency, and relevance.
Frequently asked questions
We use industry-standard techniques that show which factors drive predictions and how: SHAP values for local and global feature attributions, Partial Dependence/ICE plots to visualise marginal effects, and Friedman–Popescu H-statistics to quantify interaction strength between variables. Each model ships with plain-English documentation (model “cards”) summarising intent, performance and limits to support governance.
We combine trusted UK public datasets with proprietary market data. Typical sources include: ONS (e.g., Census 2021 and topic summaries), DfT road traffic statistics, Met Office climate/weather datasets (HadUK-Grid, MIDAS-Open) and Police street-level crime data—then enrich and engineer features for pricing and insight use cases.
We evaluate models using cross-validation to ensure testing is always done on multiple splits of unseen data. We then compare the model outputs with real premiums collected in the market. In addition, we have been working closely with an experienced pricing professional over an extended period to interrogate and validate the findings, ensuring they are both robust from an actuarial point of view and commercially meaningful.
Yes. We support standalone or API-based integration: RESTful endpoints with OpenAPI/Swagger contracts so your teams can connect rating engines, data warehouses or BI tools without custom glue code.
AI insight from our experts
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Beyond accuracy: Why explainable AI (XAI) is the future of insurance insights
The insurance industry is investing more than ever in machine learning. Pricing models are becoming more powerful, more granular, and more dynamic. From how insurers assess risk to how they compete in the market, machine learning is driving a new wave of sophistication.
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