Computer Vision Development in London

We developed and deployed a claims damage assessment platform over 14 weeks. (1) Motor damage assessment: deep learning model trained on 2.8 million annotated motor claim images (the insurer's 5-year claims history). Damage detection: identifying damaged components (bonnet, bumper, wing, door, wheel, glass, lights) with 94% component-level accuracy. Severity classification: categorising damage as minor (cosmetic), moderate (structural component damaged but repairable), and severe (potential write-off) with 91% accuracy. Repair cost estimation: predicting repair cost ranges based on damaged components, severity, and vehicle make/model — using pricing data from 2,400 approved repair network garages. The model produced estimates within 15% of actual repair cost for 82% of claims (comparable to experienced claims handler accuracy). Write-off assessment: automated total loss assessment for clearly written-off vehicles — identifying cases where repair cost would exceed the vehicle's market value. (2) Property damage assessment: models trained on 1.

2 million property claim images covering UK building types and damage categories. Damage classification: identifying damage type (water, fire, storm, impact, subsidence) and affected building elements (roof, walls, windows, flooring, ceilings, electrics). UK building knowledge: models trained to recognise UK-specific construction — slate vs. tile roofing, cavity wall vs. solid wall construction, period features vs. modern materials. Severity estimation: property damage severity scoring (1-5 scale) correlating with historical repair costs for similar damage on similar properties. Loss adjuster triage: automated determination of whether a site visit was required — simple claims (clear damage, below threshold) processed from photos only, complex claims flagged for loss adjuster visit with pre-populated assessment to guide the inspection. (3) Reserve automation: ML-based reserve estimation combining visual damage assessment with claim metadata (policy type, location, vehicle age, property age).

Motor reserves: automated initial reserve setting for standard claims — within 8% of ultimate claim cost (from 28% variation under manual assessment). Property reserves: automated initial reserve for assessed claims — within 12% of ultimate cost (from 35% variation). Confidence scoring: each reserve accompanied by a confidence score — high-confidence reserves applied automatically, low-confidence reserves reviewed by experienced handlers. (4) Visual fraud detection: image forensics — detecting manipulated photos (clone stamping, splicing, metadata inconsistencies indicating photos from different times/locations than claimed). Damage pattern analysis: identifying pre-existing damage (rust around damaged areas suggesting old damage, weathering inconsistent with claimed date), staged damage patterns (damage patterns inconsistent with claimed cause — e.g., "parking damage" on areas inconsistent with another vehicle's contact), and repeat patterns (linking photos across claims to detect organised fraud — the same vehicle, location, or damage pattern appearing in multiple claims).

Fraud scoring: every claim scored for fraud probability — high-risk claims automatically escalated to the fraud investigation team with specific visual evidence flagged. (5) Customer instant assessment: real-time damage assessment through the mobile app — claimants received preliminary damage assessment within 60 seconds of uploading photos. Guided photo capture: the app guided claimants to capture the right photos (4 corners, close-ups of damage, odometer, VIN plate) — improving photo quality and reducing the need for re-requests. Instant next steps: based on the AI assessment, claimants immediately received next steps — repair network referral (with nearest approved garage), write-off indication (with valuation range), or loss adjuster scheduling (for claims requiring site visit).