Full claims intake pipeline, from document to adjuster queue.
A mid-size insurer processing 200+ claims daily faces a bottleneck that compounds during storm seasons. Each claim requires extracting data from photos, PDFs, and handwritten forms, then cross-referencing policy details and running fraud checks before routing to the right adjuster. Adjusters spend roughly 70% of their day on document intake rather than assessment work.
The industry is under pressure from regulators mandating faster turnaround and policyholders expecting digital-first experiences. Incumbents rely on legacy claims management systems never designed for unstructured document intake. Manual data entry introduces error rates averaging 5-8%, and fraud detection depends on individual adjuster experience rather than pattern analysis across the full claims corpus.
Tractable has demonstrated that computer vision can assess vehicle and property damage from photos. Shift Technology uses ML to flag suspicious claim patterns. The question is no longer whether AI works for claims — it is how fast a carrier can integrate these capabilities without a multi-year platform migration.
This agent ingests claim documents from any channel — email attachments, portal uploads, fax scans, mobile photos — and runs layout-aware extraction using transformer-based document understanding models (LayoutLM architecture) to pull policy numbers, claimant details, incident dates, damage descriptions, and supporting evidence without per-template configuration.
It cross-references your policy administration system in real time, verifying coverage terms, deductibles, and exclusions. Simultaneously, it runs anomaly scoring against your historical claims data using gradient-boosted models trained on your own loss history, surfacing claims that match known fraud typologies — staged accidents, inflated repair estimates, phantom claimants.
The agent integrates via API with Guidewire ClaimCenter, Duck Creek, and Majesco. Clean claims are auto-routed to the appropriate adjuster queue with a structured summary: what was claimed, what the policy covers, and any flags. Adjusters open a claim and see structured data ready for a decision.
A senior engineer integrates directly with your claims management system (Guidewire, Duck Creek, or Majesco) and policy database via REST or SOAP APIs. Document extraction uses LayoutLM fine-tuned on your specific document types — claim forms, repair estimates, medical records — during a 2-week configuration phase. Fraud scoring runs on XGBoost models trained on your historical claims corpus, served through a FastAPI layer with Redis-backed caching for sub-200ms adjuster routing decisions. Full deployment typically takes 3–4 weeks.
LayoutLM processes PDFs, photos, scanned ACORD forms, and handwritten documents by understanding spatial layout, not just text. Structured fields — claimant info, loss description, damage estimates — are extracted without per-template configuration. Handles multi-page documents and mixed-quality scans common in field-submitted claims.
XGBoost models train on your historical claims data to surface anomalies before they reach an adjuster: duplicate submissions, inflated repair estimates, mismatched claimant identities, and staged-incident patterns. Each claim gets a scored risk flag with supporting evidence fields, not just a binary pass/fail. Thresholds are tunable per coverage line.
Queries your policy administration system in real time to validate coverage terms, deductibles, sublimits, and exclusions against the active policy at the date of loss. Flags coverage gaps or endorsement conflicts before the claim advances. Works against Guidewire PolicyCenter, Duck Creek Policy, or any system with a queryable API.
Routes each claim to the right queue using claim type, estimated complexity score, coverage line, and live adjuster workload data. Generates a pre-filled assessment summary — extracted fields, fraud score, coverage status — so adjusters start from structured context, not raw documents. Routing rules are configurable without code changes.
Anonymized work with hard metrics — NDA-bound, no client names.
72%
Processing Time Reduction
94%
Classification Accuracy
1.2 days
Avg Processing Time
“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 Division
We custom-build each agent to fit your data, your rules, and your existing systems.
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Fordel Studios