Multi-Agent AI Fraud Detection & Risk Assessment Engine
Deploys 7 specialized AI agents that analyze different fraud dimensions in parallel with real-time threat intelligence feeds. Provides reasoning chain transparency for explainability, multi-agent collaboration with inter-agent messaging, and human-in-the-loop intervention for critical decisions.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Credit Application Input - AI-powered fraud detection workflow with risk profile selection, device intelligence capture, and multi-agent system overview
AI Agent Orchestration Console - Real-time multi-agent fraud detection with tool execution monitoring and agent activity tracking
Credit Decision Analysis - APPROVE decision with risk score 15, comprehensive investigation summary, key findings, and recommended actions
Agent Details View - Detailed breakdown of 6 specialist agents with processing times, weights, findings, and data sources
AI Agents
Specialized autonomous agents working in coordination
Multi-Agent Orchestration Coordinator
Fragmented fraud detection signals from multiple specialist agents need synthesis into a unified, actionable decision with proper workflow coordination and audit trail maintenance.
Core Logic
Acts as the central coordinator managing all 6 specialist agents. Synthesizes findings from identity, device, behavioral, network, credit, and compliance agents into a final fraud risk score. Manages workflow execution with ~500ms processing time, handles human intervention points, generates final recommendations (APPROVE/DECLINE/MANUAL_REVIEW/REFER), and maintains comprehensive audit trails for regulatory compliance.
Identity Verification Specialist
Synthetic identity fraud and identity theft require robust validation against authoritative data sources to detect fabricated or stolen identities before credit is extended.
Core Logic
Performs NI number format validation against HMRC patterns, cross-references electoral roll database (46M+ records), analyzes address history for tenure and stability, and searches identity graphs for duplicate applications. Calls `electoral_roll_lookup`, `ni_validation_check`, `identity_graph_search`, and `identity_ml_scorer` tools. Uses DVLA data (50M+ records) with ~1200ms processing time.
Device Risk & Reputation Analyzer
Fraudsters use bots, emulators, VPNs, and compromised devices to mask their identity and location. Detecting device-level fraud signals is critical for preventing automated attacks.
Core Logic
Analyzes device fingerprints against global device database (2.5B+ devices), detects VPN/proxy usage, identifies emulators and virtual machines, scores device reputation, and validates browser fingerprinting. Calls `device_reputation_db`, `ip_geolocation_service`, `vpn_detection_engine`, and `emulator_detection_ml` tools. Uses ThreatMetrix threat intelligence with ~800ms processing time.
Session Behavior & Biometric Analyzer
Bot-driven attacks and automated form filling exhibit distinct behavioral patterns that differ from genuine human interaction. Detecting non-human behavior is essential for fraud prevention.
Core Logic
Analyzes keystroke dynamics (dwell time 80-120ms, flight time, typing speed <100 CPM), mouse movement patterns (straight-line ratio, click accuracy), copy-paste events, and session anomalies. Uses ML-based bot detection via `keystroke_biometrics_engine`, `bot_detection_model`, and `session_anomaly_detector` tools with ~600ms processing time.
Fraud Ring & Network Link Detection
Organized fraud rings operate through connected networks of applications sharing common attributes. Identifying these connections is crucial for detecting coordinated fraud schemes.
Core Logic
Traverses entity graph database (500M+ nodes) using Neo4j, applies graph neural networks for fraud ring detection, performs velocity checks across consortium members, and analyzes fraud ring clustering and centrality. Calls `entity_graph_neo4j`, `fraud_ring_detection_gnn`, `cifas_consortium_api`, and `velocity_analyzer` tools with ~1500ms processing time.
Credit Scoring & Synthetic Identity Detection
Thin-file and synthetic identities present unique credit risk challenges. Accurate creditworthiness assessment and synthetic ID detection protect against bust-out fraud schemes.
Core Logic
Calculates FICO scores from bureau data, analyzes credit utilization and payment history, evaluates debt-to-income ratios, and detects synthetic identities through thin file age and new tradeline patterns. Calls `nexgile_credit_pull`, `experian_credit_pull`, `affordability_calculator`, `synthetic_id_detector`, and `dti_lti_scorer` tools with ~1100ms processing time.
Regulatory Compliance & Watchlist Screening
Financial institutions must comply with KYC/AML regulations and screen applicants against PEP lists, sanctions databases, and regulatory watchlists to avoid regulatory penalties and reputational damage.
Core Logic
Performs KYC verification, PEP screening against Dow Jones database (1.5M+ entries), OFAC/UN/EU/HMT sanctions checking via Refinitiv World-Check (50K+ records), AML risk scoring, age verification (18+ requirement), and FCA Consumer Duty assessment. Calls `dow_jones_pep_screening`, `refinitiv_sanctions_check`, `aml_risk_scorer`, and `age_verification` tools with ~900ms processing time.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
8 technologies
Architecture Diagram
System flow visualization