Healthcare Claims Processing: 70% Faster with AI

The Problem

A regional health insurer was drowning in paper and PDF claims. Every claim required manual data entry into their core system—policy numbers, procedure codes, amounts, and patient details. A team of 12 processors handled 2,000+ claims per week, with an average turnaround of 4–5 days. Errors from manual entry led to rework, delayed payments, and frustrated providers.

The Solution

We built a document AI pipeline that:

1. Extracts structured data from scanned PDFs and images using a combination of OCR and LLM-based extraction
2. Validates against policy rules in real time, flagging anomalies for human review
3. Routes claims automatically to the right queue based on complexity and amount
4. Pre-fills the core system via API integration, reducing manual entry to verification only

The system uses a RAG-style approach: policy documents and coding guidelines are embedded and retrieved when the model needs to resolve ambiguous codes or apply business rules.

The Outcome

• 70% reduction in average processing time (4.5 days → 1.3 days)
• 40% fewer data-entry errors thanks to structured extraction and validation
• 3 FTEs redeployed from data entry to exception handling and quality review
• ROI in 8 months from labor savings alone

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