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Catching $200K in Insurance Underpayments with Automated Document Comparison

An AI-powered document comparison engine that OCRs submitted claims against received EOBs — catching 95% of discrepancies automatically and recovering $200K in underpayments in the first year.

Build Your Document Comparison Engine

About the Project

An automated document comparison platform that uses OCR and AI to compare submitted claims against received Explanations of Benefits and highlight every discrepancy. It surfaces underpayments, incorrect denials, and silent code changes, generating appeal-ready reports that recover lost revenue.

Industry

Healthcare & Insurance Billing

Claim-to-EOB reconciliation for practices that bill insurance every month.

  • Insurance Billing
  • Revenue Recovery
  • Payer Appeals
Business Type

Dental and medical practices with insurance billing operations

Practices receiving hundreds of EOBs a month with no way to check them.

  • Private Practice
  • In-House Billing
  • Multi-Carrier Mix
Core Offering

AI-powered claim vs. EOB comparison with automated discrepancy detection

OCR extracts both documents, then AI highlights every field that does not match.

  • OCR Extraction
  • Field Mapping
  • Discrepancy Flags
  • Batch Comparison
Recovery Workflow

From detection to appeal in one pass

Appeal-ready reports and pre-filled letters lifted appeal success from 30% to 72%.

  • Appeal Templates
  • Underpayment Alerts
  • Carrier Patterns
  • Status Tracking
Results

$200K Recovered in Year One

95% of discrepancies caught automatically versus 20% under manual review.

  • 95% Caught
  • 72% Appeal Success
  • 3 Days to 2 Hours
Build your idea

Talk to our experts

Scope your own claim-to-EOB comparison engine with our healthcare billing team.

  • Free Consultation

The Problem: Silent Underpayments

Underpayments went unnoticed and unrecovered.

Silent Loss
Practices receive Explanation of Benefits (EOBs) from insurance companies but rarely compare them against submitted claims — leading to silent underpayments, incorrect denials, and missed revenue that accumulates month after month.
Unprovable
A practice manager in Florida had a nagging feeling they were being underpaid but couldn't prove it. Manually comparing hundreds of EOBs against claims each month was impossible with existing staff, so discrepancies went undetected and unrecovered.
3d to 2hr
The objective was to build an automated comparison engine that could OCR both documents, extract key fields, highlight every discrepancy, and generate appeal-ready reports — turning a 3-day manual process into a 2-hour automated one.

From undetected underpayments
to automated discrepancy catching
and $200K in recovered revenue

Recover Your Lost Revenue

Our Solution

  • OCR-Powered Document Extraction

    OCR-Powered Document Extraction

    OCR-Powered Document Extraction

    • Upload submitted claim and received EOB for side-by-side comparison
    • OCR extraction of key fields (amounts, codes, dates, patient info)
    • Automatic field mapping between claim & EOB formats across carriers
  • AI-Highlighted Discrepancy Detection

    AI-Highlighted Discrepancy Detection

    AI-Highlighted Discrepancy Detection

    • AI-flagged discrepancies: billed vs. paid, codes changed, items denied
    • Auto-flagging of underpayments exceeding configurable threshold amounts
    • Pattern detection across carriers for systematic underpayment trends
  • Batch Reconciliation & Audit Trail

    Batch Reconciliation & Audit Trail

    Batch Reconciliation & Audit Trail

    • Batch comparison for monthly reconciliation of all claims vs. EOBs
    • Full comparison history and audit trail for compliance documentation
    • Dashboard: total underpayments, recovery rates & outstanding appeals
  • Appeal-Ready Report Generation

    Appeal-Ready Report Generation

    Appeal-Ready Report Generation

    • Export comparison reports formatted for insurance appeals
    • Auto-populated appeal letter templates with discrepancy details
    • Track appeal submission status and resolution outcomes
  • Recovery Analytics & Payer Trends

    Recovery Analytics & Payer Trends

    Recovery Analytics & Payer Trends

    • Underpayment totals tracked by carrier, code, and location
    • Repeat patterns escalated as contract-level disputes
    • Recovery rate and appeal outcomes on one dashboard

Challenges: Invisible Revenue Loss and Manual Reconciliation

Hover a row to see what changed.

Why This Platform Turns Lost Revenue Into Recovered Income

We built an AI-powered comparison engine that automates the entire claim-to-EOB reconciliation workflow — from OCR extraction to discrepancy detection to appeal-ready reporting.

The Impact: Recovering Revenue That Was Silently Lost

Every number below was measured in production after launch — not projected in a pitch deck.

$200K

Underpayments Found — In the First Year

95%

Discrepancies Caught — Automatically vs. 20% Manual

72%

Appeal Success Rate — Up from 30%

93%

Time Reduction — Monthly Reconciliation

Global presence

Three offices. One team.

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