FraudGuard AI - Healthcare Claims Fraud Detection System
FraudGuard AI deploys an 8-agent orchestrated pipeline leveraging Claude 3.5 Sonnet, XGBoost, Isolation Forest, GraphSAGE neural networks, and Bayesian inference.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Claim Intake - Submit healthcare claim data for AI-powered fraud analysis with 8 specialized agents
Multi-Agent Pipeline - Real-time processing through 8 specialized AI agents with live execution monitoring
Human-in-the-Loop Decision Support - Review AI findings with explainability and model confidence metrics
Compliance & Audit Trail - Complete audit logging with encryption, tamper-evident records, and regulatory compliance verification
AI Agents
Specialized autonomous agents working in coordination
Orchestrator Agent
Complex fraud detection requires coordinated analysis across multiple specialized domains. Without central orchestration, agents operate in silos, miss cross-domain fraud patterns, and produce inconsistent results. Pipeline failures can halt processing without graceful recovery.
Core Logic
Coordinates the multi-agent pipeline using Claude 3.5 Sonnet. Routes claims to appropriate agents based on claim characteristics, manages workflow state across 4 pipeline stages (Data Ingestion, Validation, Analysis, Synthesis), handles inter-agent communication via message queues, aggregates findings from all agents, and implements error handling with automatic retry mechanisms. Achieves 99.2% accuracy with 45ms average latency.
Data Extraction Agent
Healthcare claims arrive in multiple formats including EDI 837, PDF attachments, scanned documents, and portal submissions. Manual data entry is error-prone and slow. Inconsistent data formats prevent downstream analysis and increase processing time.
Core Logic
Combines Claude 3.5 Sonnet with Tesseract OCR to extract and normalize data from all claim formats. Performs intelligent entity recognition for medical codes (ICD-10, CPT, HCPCS), provider identifiers (NPI, Tax ID), and patient demographics. Maps codes to standardized vocabularies and validates data completeness. Processes PDF attachments, operative reports, and authorization letters. Achieves 96.8% accuracy with 320ms average latency.
Eligibility Verification Agent
Processing claims for ineligible members or non-covered services wastes resources and leads to payment errors. Manual eligibility checks are slow and may use stale data. Coordination of benefits (COB) scenarios require complex logic to determine primary payer.
Core Logic
Uses Claude 3.5 Haiku for rapid eligibility lookup against member databases and real-time 270/271 EDI transactions. Verifies coverage dates, benefit limits, and service-specific authorization requirements. Detects COB situations and determines payment order. Validates provider network status and calculates member cost-sharing. Achieves 99.7% accuracy with 89ms average latency.
Clinical Validation Agent
Fraudulent claims often involve clinically implausible diagnosis-procedure combinations or services lacking medical necessity. Rule-based systems cannot capture nuanced clinical relationships. Without clinical validation, inappropriate payments occur.
Core Logic
Employs a medically fine-tuned Claude 3.5 Sonnet model trained on clinical guidelines, LCD/NCD policies, and coding rules. Validates diagnosis-procedure pair appropriateness, evaluates medical necessity using evidence-based criteria, checks age/gender appropriateness, and identifies clinically impossible combinations. References CMS Local Coverage Determinations and National Coverage Decisions. Achieves 94.2% accuracy with 456ms average latency.
Billing Pattern Analyzer
Sophisticated billing fraud schemes (upcoding, unbundling, modifier abuse) evade rule-based detection by staying within individual claim limits. Without historical pattern analysis and peer comparison, systematic abuse goes undetected.
Core Logic
Deploys an ensemble of XGBoost and Isolation Forest models to detect billing anomalies. Compares provider billing patterns against specialty-specific peer benchmarks. Identifies upcoding through E&M level distribution analysis, detects unbundling via NCCI edit evaluation, and flags modifier abuse patterns. Calculates statistical deviation scores with Z-score thresholds. Achieves 97.6% accuracy with 234ms average latency.
Network Analysis Agent
Organized fraud rings involve coordinated billing across multiple providers, facilities, and patients. Traditional claim-by-claim analysis cannot detect network-level collusion patterns, kickback arrangements, or referral concentration schemes.
Core Logic
Utilizes GraphSAGE neural networks with Neo4j graph database to model provider-facility-patient relationships. Computes network centrality metrics, detects community structures, identifies referral concentration patterns, and flags suspicious entity clusters. Cross-references ownership data and shared addresses. Visualizes provider networks with risk-scored nodes and relationship edges. Achieves 91.8% accuracy with 567ms average latency.
Fraud Scoring Agent
Individual agent findings must be synthesized into a unified fraud probability with calibrated confidence intervals. Without proper evidence aggregation, decision thresholds are arbitrary and inconsistent across claim types.
Core Logic
Implements Bayesian Neural Network for evidence synthesis and fraud probability calculation. Aggregates findings from all upstream agents with learned weights. Generates calibrated probability scores with confidence intervals. Computes feature importance rankings for decision explanation. Outputs prior probability, likelihood ratios, and posterior fraud probability. Achieves 98.1% accuracy with 178ms average latency.
Explainability Agent (XAI)
Black-box AI decisions are unacceptable for healthcare payment determinations. Regulators require audit trails and human-understandable explanations. Claims examiners need actionable insights to validate AI recommendations.
Core Logic
Combines Claude 3.5 Sonnet with SHAP and LIME libraries to generate comprehensive explanations. Computes SHAP values showing each feature's contribution to the fraud score. Generates counterfactual explanations (what would change the decision). Produces natural language narratives suitable for audit documentation and examiner review. Creates visualizations including waterfall charts and feature importance plots. Achieves 95.4% accuracy with 289ms average latency.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
8 technologies
Architecture Diagram
System flow visualization