Claims & EOI Automation Guide

Claims Intake Automation

Automating claims intake begins with intelligent document processing. AI models can extract structured data from claim forms, medical records, police reports, and supporting documentation regardless of format. Using a combination of OCR, natural language processing, and classification models, the system can categorize incoming claims by type, severity, and required handling path within seconds of receipt.

Build your intake automation around a triage engine that scores each claim for complexity and routes it accordingly. Simple, straightforward claims can proceed through straight-through processing with minimal human intervention, while complex or high-value claims are routed to specialized adjusters with AI-generated summaries and preliminary assessments that accelerate their review.

Fraud detection should be embedded directly into the intake pipeline rather than applied as a post-processing step. ML models trained on historical fraud patterns can flag suspicious claims for enhanced review while allowing legitimate claims to proceed without delay. This parallel approach maintains processing speed while strengthening fraud prevention.

EOI Processing Pipeline

Evidence of insurability processing is one of the most labor-intensive workflows in group benefits administration. An automated EOI pipeline uses AI to extract applicant information, match it against underwriting guidelines, and generate preliminary decisions for straightforward cases. This can reduce EOI processing time from weeks to days for the majority of applications.

Design the pipeline with configurable decision rules that underwriters can adjust without engineering support. The AI layer handles data extraction and matching, while business rules determine approval thresholds, required documentation, and escalation criteria. This separation of concerns allows the system to adapt to changing underwriting guidelines without model retraining.

Measuring Success

Track automation success through a balanced scorecard that covers processing speed, accuracy, customer satisfaction, and cost per claim. Establish baselines for each metric before automation and measure improvement over rolling thirty-day windows. Target a straight-through processing rate of seventy percent or higher for standard claims within the first six months of deployment.

Monitor model performance continuously by comparing AI decisions against human reviewer outcomes. Calculate precision and recall for fraud detection, routing accuracy for claim triage, and extraction accuracy for document processing. Implement automated retraining triggers when performance metrics drift below established thresholds to maintain consistent quality over time.