AI Claims Adjudication Digital Worker
Deploys 7 specialized AI agents orchestrated in sequence to analyze claims from document intake through final decision. Each agent applies ML models and reasoning chains, communicating findings via inter-agent messaging.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Claims Risk Assessment - Multi-agent orchestration workflow
Claims Risk Assessment - Document analysis and clinical validation
Claims Risk Assessment - Fraud detection and cost analysis
Claims Risk Assessment - Final decision and compliance review
AI Agents
Specialized autonomous agents working in coordination
Orchestrator Agent
Coordinates the complex multi-agent workflow ensuring proper sequencing, handles routing decisions based on claim characteristics, and aggregates findings from all specialist agents into a coherent final decision.
Core Logic
Receives claim data, validates input format, determines required analysis paths based on claim type and amount. Routes to specialist agents in sequence, monitors execution, synthesizes multi-agent findings using weighted confidence scoring, and renders final decision (APPROVE, DENY, PARTIAL_APPROVE, ESCALATE, or SIU_REFERRAL).
Document Analysis Agent
Claims arrive with attached documents (CMS-1500 forms, medical records, receipts) that must be verified for authenticity, checked for tampering, and parsed for structured data extraction.
Core Logic
Uses neural OCR (document-ocr-transformer-v2) to extract text with 97%+ confidence. Runs forgery detection models to identify tampering, validates signatures against provider records, checks date consistency, identifies missing required documents, and extracts structured fields (procedure codes, diagnosis, dates) for downstream agents.
Clinical Validation Agent
Medical claims must be validated against clinical guidelines and medical policies. Incorrect coding, unbundling, and medically unnecessary procedures cause overpayments and compliance risks.
Core Logic
Loads ICD-10/CPT code databases and CMS guidelines. Cross-references diagnosis codes with procedures, validates medical necessity using BERT model (clinical-necessity-bert-v3), checks for proper code bundling, verifies service levels match documented conditions, and identifies guideline violations (e.g., MRI without prior conservative treatment).
Fraud Detection Agent
Healthcare fraud costs billions annually. Individual claims examiners cannot detect sophisticated fraud schemes, statistical anomalies, or cross-fund patterns that indicate coordinated fraud rings.
Core Logic
Runs XGBoost anomaly detection model (fraud-detection-xgboost-v2) against 127 known fraud scheme patterns. Queries provider history across 220 Nexgile-RiskMind funds, calculates statistical deviations from peer groups, identifies unbundling and upcoding patterns, detects billing rate outliers, and generates cross-fund network alerts for coordinated investigation.
Cost Analysis Agent
Claims are often billed above fair market rates. Without automated comparison to fee schedules and regional benchmarks, funds overpay for services.
Core Logic
Loads Medicare fee schedules and regional PPO rates. Performs line-item cost analysis against expected values, calculates variance percentages, determines recommended reimbursement amounts using contracted rates, computes member responsibility (copays, deductibles, coinsurance), and generates savings breakdown by category (fee reduction, medical necessity, fraud prevention).
Compliance Agent
Claims processing must comply with HIPAA privacy rules, CMS billing regulations, ERISA fiduciary requirements, ACA mandates, and state-specific insurance laws. Manual compliance tracking is error-prone.
Core Logic
Loads regulatory frameworks (HIPAA, CMS, ERISA, ACA, state regulations). Validates PHI handling, checks prior authorization requirements, verifies timely filing compliance, audits CMS Correct Coding Initiative rules, confirms plan document adherence, and generates compliance checklists with pass/fail status for each regulatory requirement.
Appeal Prediction Agent
Denied claims often result in costly appeals. Without predictive analytics, funds cannot anticipate which denials will be contested or prepare defensible documentation.
Core Logic
Queries 50,000+ historical claims database matching by diagnosis, procedure, and provider. Uses XGBoost model (appeal-prediction-xgboost-v4) to calculate appeal likelihood, predicts success probability based on similar case outcomes, analyzes member behavior patterns, recommends partial approval strategies to reduce appeal burden, and estimates cost of appeal processing.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
9 technologies
Architecture Diagram
System flow visualization