What You’ll Build
In roughly two hours, you will have a claims processing pipeline that accepts claim forms, supporting documents, and damage photos, extracts structured data from every submission, cross-references policy details, flags potential fraud indicators, and routes claims for approval or investigation. A single dedicated GPU server processes 800 claims per hour with all policyholder data remaining on your own infrastructure — essential for regulatory compliance in insurance.
Manual claims handling costs insurers $15-30 per claim in adjuster time, and processing backlogs after major events can stretch resolution times to weeks. GPU-accelerated AI on open-source models reduces first-touch handling time by 80% while catching fraud signals that overwhelmed adjusters miss during volume surges.
Architecture Overview
The pipeline has four stages: document intake with OCR, data extraction and structuring, validation and fraud scoring, and decision routing. PaddleOCR processes scanned claim forms, handwritten notes, and photographed documents. An LLM through vLLM interprets OCR output into structured claim records — claimant details, incident description, damage list, claimed amounts, and supporting evidence references.
A vision model analyses damage photographs to estimate severity and cross-check against claimed amounts. The fraud detection module scores each claim across known patterns: recently opened policies, repeated claim types, inconsistent timelines, and amount anomalies relative to incident type. Claims meeting auto-approval criteria route directly to payment, while flagged claims queue for human review with AI-generated summaries explaining each flag.
GPU Requirements
| Claim Volume | Recommended GPU | VRAM | Processing Speed |
|---|---|---|---|
| Up to 200/day | RTX 5090 | 24 GB | ~12 claims/min |
| 200 – 1,000/day | RTX 6000 Pro | 40 GB | ~30 claims/min |
| 1,000+/day | RTX 6000 Pro 96 GB | 80 GB | ~60 claims/min |
Vision analysis of damage photos requires additional VRAM alongside the text extraction model. A 40GB card comfortably runs both an 8B text model and a vision model concurrently. Check our self-hosted LLM guide for multi-model deployment strategies.
Step-by-Step Build
Deploy PaddleOCR and vLLM on your GPU server. Set up a claim intake endpoint through your API layer that accepts document uploads. Build the extraction, validation, and routing pipeline.
# Claim extraction prompt
EXTRACT_PROMPT = """Extract structured claim data from this document.
OCR text: {ocr_output}
Return JSON:
{claimant: {name, policy_number, contact},
incident: {date, location, type, description},
damages: [{item, description, claimed_amount}],
total_claimed, supporting_docs: [string],
witnesses: [{name, contact}]}"""
# Fraud scoring prompt
FRAUD_PROMPT = """Assess fraud risk for this claim.
Claim data: {claim_json}
Policy details: {policy_json}
Claim history: {history_json}
Score 0-100 fraud risk. Flag specific indicators:
- timeline_consistency (incident vs report dates)
- amount_reasonability (claimed vs typical for type)
- policy_recency (days since policy inception)
- pattern_match (similar past claims)
Return JSON with risk_score, flags[], recommendation."""The routing engine applies configurable business rules: low-risk claims under a threshold auto-approve, medium-risk claims go to a junior adjuster with AI summaries, and high-risk claims route to the special investigations unit. See vLLM production setup for throughput optimisation during surge events.
Compliance and Audit
Insurance regulations require explainable decisions and complete audit trails. Every AI assessment includes written reasoning that adjusters can review and override. The system logs all extraction results, fraud scores, and routing decisions with timestamps and model versions. Because everything runs on your dedicated GPU, policyholder medical records, financial details, and personal information never leave your controlled environment.
Regular model evaluation against labelled claim outcomes tracks accuracy over time. Retrain fraud detection thresholds quarterly using your own claims data to adapt to emerging patterns specific to your book of business.
Deploy Your Claims Processor
Automated claims processing cuts average handling time from days to minutes while improving fraud detection rates. Keep all policyholder data on-premises and maintain full regulatory compliance. Launch on GigaGPU dedicated GPU hosting and modernise your claims workflow. Browse more automation patterns and integration tutorials to extend your pipeline.
