Claims Ingestion & Extraction with AI

Using AI in Claims Ingestion & Extraction

According to research published in the International Journal of Science and Research Archive, "healthcare claims take 30-90 days to be approved" with "22% of billing errors in healthcare result[ing] in claim denials and disputes" with "manual data entry [is] prone to human errors, slowing down claims and policy approvals" (ItechUS Inc., January 2025).

Similarly, an auto insurance company faced these exact challenges with an extremely manual claim estimates ingestion and extraction process. Due to various forms, emails, and PDF templates from different body shops (Vspec, Tesla Collision Paramus, Blocker's Collision LLC, etc.), the existing workflow was high effort, highly susceptible to human error, and cumbersome.

Liventus built a custom AI-powered workflow using Azure Document Intelligence and Azure OpenAI that integrates seamlessly with existing SQL and Windows systems, performing template-agnostic extraction that continuously improves through reviewer corrections.

AI Use: Why Intelligence Matters Here

Auto insurance claim estimates arrive in dozens of different formats. Each body shop uses its own template with different section names (Materials vs. Labor), varying layouts, and inconsistent data structures.

Traditional Optical Character Recognition, or OCR, and rule-based extraction fail in many instances because they require pre-defined templates for each format. When a new shop's estimate arrives, someone must manually configure extraction rules or resort to manual data entry.

AI-powered document intelligence doesn't need templates, it understands document structure contextually, identifies relevant data fields regardless of formatting, adapts to new document types automatically, and learns from human corrections to improve accuracy over time.

This enables true template-agnostic processing where the system handles any estimate format without additional configuration.

Business Impact & Applications

• Processing Speed: Automate extraction of claim estimates sent via email, eliminating manual data entry bottlenecks

• Error Reduction: Remove human transcription errors that cause downstream processing delays and claim rejections

• Template Flexibility: Handle heterogeneous PDF and email formats without configuration for each new template

• Continuous Learning: AI improves extraction accuracy through reviewer corrections and feedback loops

• Integration Simplicity: Seamless connection with existing SQL databases and workflows requiring minimal system changes

• Auto Insurance: Extract collision repair estimates from multiple body shops with varying document formats

• Property Insurance: Process contractor estimates for home damage repairs across different regional vendors

• Workers' Compensation: Ingest medical treatment estimates from diverse healthcare providers and facilities

• Commercial Lines: Handle equipment repair estimates from varied service providers and manufacturers

• Health Insurance: Extract procedure codes and costs from medical provider estimates with different billing formats

• Fleet Insurance: Process vehicle maintenance and repair estimates from nationwide service networks

Future of AI in Claims Processing

As AI capabilities advance, claims processing will shift from document extraction to intelligent claims decisioning. Future platforms will automatically assess claim validity based on historical patterns, predict settlement amounts using similar claim data, detect fraudulent submissions through anomaly detection, and orchestrate entire claims workflows from First Notice of Loss through final payment without human intervention for straightforward cases.

Ready to Eliminate Manual Claims Data Entry?

See how AI-powered claims ingestion can transform your processing speed and accuracy. Contact us for a free AI consultation and discover how to handle any estimate format without manual configuration.