Agentic AI Multi-Agent Fraud Detection System
Deploys 14 specialized AI agents across 8 parallel execution stages using DAG-based orchestration with confidence-calibrated autonomous decision-making and human-in-the-loop escalation for edge cases..
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
AI Agents
Specialized autonomous agents working in coordination
Orchestrator Agent
Coordinating 14 specialized agents with complex dependencies requires intelligent scheduling to maximize parallel execution while respecting data dependencies.
Core Logic
Implements topological sort with critical path analysis using DAG-based dependency resolution. Organizes agents into 8 execution stages, running independent agents in parallel within each stage. Uses O(V+E) complexity for execution planning with 12.4 MB memory footprint.
Real-Time Eligibility Verifier
Claims processing without real-time eligibility verification leads to payment of ineligible claims and coordination of benefits failures.
Core Logic
Performs real-time X12 270/271 transactions combined with FHIR R4 Coverage queries. Detects COB situations, terminated coverage, dependent age-outs, and Medicare/Medicaid dual eligibility. Validates coverage effective dates against date of service.
Prior Authorization Automator
Manual prior authorization processes create delays and compliance risks as CMS 2026 mandates require electronic PA with real-time responses.
Core Logic
Implements FHIR Prior Authorization API with CRD/DTR integration. Validates PA existence, expiration status, and authorized units versus billed units. Calculates interoperability compliance scores against CMS 2026 requirements including electronic submission, real-time response, and patient access APIs.
No Surprises Act Compliance Engine
Out-of-network billing violations expose organizations to significant penalties and member harm under the No Surprises Act.
Core Logic
Calculates Qualifying Payment Amount (QPA) using median in-network rates over 90 days. Identifies balance billing violations, missing good faith estimates, and IDR-eligible disputes. Detects when billed amounts exceed 1.5x QPA threshold. Tracks patient consent for out-of-network services.
Benford's Law Statistical Analyzer
Fabricated billing amounts often exhibit unnatural digit frequency distributions that deviate from expected patterns in legitimate financial data.
Core Logic
Applies Benford's Law first-digit frequency analysis with Chi-Square hypothesis testing. Uses formula P(d) = log10(1 + 1/d) for expected distribution. Detects systematic billing manipulation through statistical deviation analysis with O(n) complexity.
Pattern Recognition Engine
Complex fraud schemes involve subtle patterns across multiple dimensions that are invisible to rule-based detection systems.
Core Logic
Combines Isolation Forest for anomaly detection with DBSCAN density-based clustering. Uses anomaly score s(x,n) = 2^(-E(h(x))/c(n)) for outlier identification. Implements epsilon-neighborhood analysis for discovering fraud clusters with O(n log n) complexity.
GLP-1 and Telehealth Fraud Detector
Emerging fraud in GLP-1 medications and telehealth services represents a rapidly growing threat with compounding fraud, pill mills, and impossible geography patterns.
Core Logic
Detects compounding fraud, off-label abuse, dosage manipulation, telehealth-only prescriber patterns, and quantity abuse. Analyzes prescriber GLP-1 prescription rates versus peer averages. Validates telehealth visit geography and timing for impossible travel detection. Calculates risk scores using weighted formula across prescription rate, telehealth exclusivity, and dosage patterns.
Medical Necessity Validator
Claims lacking medical necessity represent significant waste, but validating clinical appropriateness requires deep medical coding expertise.
Core Logic
Implements rule engine combined with NLP classification for CPT-ICD10 validation. Cross-references NCCI edit matrix, Local Coverage Determinations (LCD), and National Coverage Determinations (NCD). Uses formula Valid(CPT, ICD) = RuleSet intersection LCD intersection NCD with O(n*m) complexity.
Provider Profiler
Identifying high-risk providers requires comparing individual behavior patterns against specialty-specific peer groups across multiple dimensions.
Core Logic
Calculates Mahalanobis distance D_M = sqrt[(x-mu)T * Sigma^-1 * (x-mu)] for multivariate outlier detection. Computes Z-scores against peer group distributions. Generates provider risk scores, specialty-specific deviation metrics, and peer ranking percentiles.
Network Analyzer
Collusion between providers, facilities, and referring physicians creates fraud rings that individual claim analysis cannot detect.
Core Logic
Applies PageRank algorithm PR(u) = (1-d)/N + d * Sum[PR(v)/L(v)] for entity importance scoring. Uses Louvain modularity optimization for community detection to identify fraud rings. Performs graph-based relationship analysis with O(V+E) complexity.
Financial Impact Calculator
Quantifying fraud exposure and recovery potential requires actuarial-grade financial modeling with uncertainty quantification.
Core Logic
Calculates fair market value using RVU-based pricing: FMV = RVU * CF * GPCI. Runs Monte Carlo simulations for expected recovery E[Recovery] = Sum[p_i * r_i]. Generates confidence intervals for financial exposure estimates.
Autonomous Decision Engine
Human review of every fraud alert is impractical at scale, but autonomous decisions require careful calibration to avoid false positives.
Core Logic
Implements confidence-calibrated autonomous decision-making with configurable thresholds. Auto-approves decisions above 92% confidence, escalates to human review below 75% confidence. Maintains adaptive learning from human override patterns to continuously improve accuracy.
Predictive Risk Forecaster
Reactive fraud detection catches problems after damage occurs; proactive intervention requires forecasting which providers will become high-risk.
Core Logic
Combines LSTM neural networks for temporal pattern learning with XGBoost gradient boosting for static feature analysis. Predicts provider risk trajectories at 30-day and 90-day horizons using formula Risk_t+30 = LSTM(Features_t) + XGB(Static). Generates early warning signals with days-until-critical estimates.
Evidence Compiler
Fraud investigations require comprehensive, legally-defensible documentation packages compiled from multiple agent outputs.
Core Logic
Generates legal-grade documentation using template engines with PDF output. Compiles audit trails across all agent decisions. Calculates composite evidence scores: EvidenceScore = w1*Statistical + w2*Clinical + w3*Network. Produces investigation-ready packages for SIU referral.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
5 technologies
Architecture Diagram
System flow visualization