Document Extraction Agent
Extracts and validates critical fields from uploaded documents to fast-track claims and servicing tasks
Description
Challenge:
Claims, endorsements, and servicing workflows often rely on manually reviewing documents like discharge summaries, bills, ID proofs, and policy documents. This process is time-consuming, error-prone, and leads to bottlenecks in claim adjudication, customer service, and compliance validation. Variability in document formats—especially scanned and handwritten content—further increases turnaround time.
How It Works:
The agent instantly activates when users upload documents during claims, KYC, or service requests. It uses OCR (Optical Character Recognition) and AI-based field mapping to extract key data points—like treatment dates, diagnosis, provider name, policy number, or claim amount—from unstructured or semi-structured documents. It connects to the document prediction module for label mapping and classification. The extracted data is validated against system-of-records (like PAS, CRM, or claims engines) to ensure consistency. Any mismatches trigger alerts or auto-initiate re-validation workflows. Structured outputs are then forwarded to downstream systems or routed to relevant agents.
Benefits:
Reduces document data extraction time by 80–90%.
Improves claim accuracy by validating extracted data before processing.
Enables straight-through processing (STP) by converting unstructured input into clean, structured formats.
Flags mismatches early to reduce rework and compliance risks.
Ensures document data consistency across claims, underwriting, and servicing.
Features
This agent streamlines document-heavy processes by transforming PDFs, images, and scans into actionable data. It supports multiple use cases including FNOL, endorsements, KYC, pre-auth, and reimbursement claims.
Features & Capabilities:
Document Type Classification: Identifies type—bill, discharge summary, ID, etc.—using ML classification.
AI-Powered Field Extraction: Extracts key fields like treatment date, billed amount, Aadhar number, etc.
Handwriting Recognition: Uses handwriting OCR models to read doctor notes or signatures.
Multi-Document Parsing: Supports batch uploads and processes documents in parallel.
Mismatch Detection: Compares extracted data with PAS or CRM entries and flags inconsistencies.
Language Agnostic Processing: Handles documents in regional languages using NLP models.
Confidence Scoring: Applies threshold-based logic to trigger human validation where needed.
Auto-Routing: Forwards structured data to Claims Agent, Policy Update Agent, or U/W queue.
Audit Trail Creation: Logs every extraction and validation step for regulatory transparency.
Attachment Consolidation: Collates relevant documents into claim/service case folders.
Eligibility Logic
Decision Logic Flow:
This agent follows a multi-step logic path—document classification, field extraction, confidence validation, and data cross-checking. Each step ensures only validated, high-confidence data proceeds to downstream processing.
Key Logic Pathways Followed:
Document Recognition: → Classifies document type (e.g., bill, ID, discharge summary).
Field Extraction & Preprocessing: → Extracts defined fields; cleans text (e.g., date formats, currency normalization).
Confidence Threshold Check: → If below threshold, routes for manual review; if above, proceeds automatically.
Data Validation: → Matches extracted fields against master data in PAS, Claims Engine, or CRM.
Mismatch Handling: → If fields conflict, flags for reconciliation workflow.
Routing Decision: → If data is clean, forwards to respective agents (e.g., Pre-Auth Agent, Policy Update Agent).
