Fordel StudiosClaims Processing Automation for Motor Insurance
This case study describes a real engagement. Client identity, proprietary details, and specific metrics are anonymized or approximated under NDA.
What needed
solving
Manual claims processing averaging 5 business days from submission to adjuster assignment. High variability in damage assessment categorization — similar damage types were being routed to different adjuster tiers, creating inconsistent settlement timelines and adjuster workload imbalances.
Motor insurance damage images vary significantly in quality — many are taken by policyholders on mobile phones under poor lighting conditions, at odd angles, or with obstructions. The classification model had to handle this noise without requiring standardized photography, which would have created a policyholder friction problem. Document parsing complexity was also significant: policy documents arrived in multiple formats (PDF, scanned images, and occasionally faxed paper scans that had been photographed) with no standardized layout across the vehicle makes and policy types in the portfolio. A secondary constraint was the existing claims management system, which had been in operation for over a decade and had no modern API — integration required a custom adapter layer built against a SOAP-based interface.
How we
built it
- 01
Audited six months of historical claims to map the decision tree adjusters were using manually — damage severity thresholds, coverage triggers, documentation requirements — and encoded these as the classification rules the model would produce.
- 02
Built a document ingestion pipeline that handles the actual variance in submission quality: low-resolution phone photos, partially completed forms, and scanned documents with alignment issues are normalised before extraction.
- 03
Trained the damage classification model on a proprietary dataset of labelled claims across the insurer's vehicle fleet distribution, not on generic public image datasets that didn't reflect the typical submission quality.
- 04
Implemented a confidence threshold for auto-routing: claims above a set confidence score route directly to the appropriate adjuster tier; below-threshold claims are flagged for manual triage with the AI assessment as a starting point.
This engagement automated the intake and initial assessment layer of a motor insurance claims operation. The system handles the full intake workflow: document parsing for policy documents, incident reports, and repair estimates; image analysis for vehicle damage classification; severity scoring against configurable routing rules; and automated assignment to the appropriate adjuster queue. The pipeline was built to integrate with the existing claims management system via API rather than replacing it, which constrained the output schema but simplified deployment. The system processes claims end-to-end in under 2 minutes from document upload to adjuster assignment queue entry.
What we
delivered
Document ingestion pipeline combined with image analysis for damage classification, producing structured claims records with automated severity scoring and adjuster routing logic.
Measurable
outcomes
- Claims processing time reduced 72% — from a 5 business day average to 1.2 days from submission to adjuster assignment.
- Damage classification accuracy reached 94%, eliminating the inter-adjuster variability that had been causing inconsistent settlement timelines.
- Adjuster workload balanced across tiers as routing decisions became based on assessed damage complexity rather than manual category guessing.
“The classification consistency was the biggest operational win. Adjusters are now working from standardised severity assessments rather than making independent calls on damage they have never seen before.”
— Head of Claims Operations, Motor Insurance DivisionReady to build
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