Real-Time Fraud Detection & Investigation Hub
Orchestrates 8 specialized AI agents in a real-time pipeline that computes 42 features, runs ensemble ML models (XGBoost + Neural Networks), provides SHAP explainability, and delivers decisions in under 500ms with full audit trails for regulatory compliance..
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Real-Time Fraud Detection Pipeline interface showing transaction input form with customer details, device intelligence, contextual signals, and pre-configured test scenarios across different risk levels for AI investigation.
Multi-Agent Orchestration Engine displaying 8 AI agents at 100% completion across pipeline phases, tool invocations timeline, inter-agent communications, and data flow summary with final CHALLENGE decision.
Investigation Results dashboard presenting CHALLENGE decision with 76.78% risk score, 6-stage execution process flow, agent contributions with key findings, and SHAP explainability waterfall showing feature importance analysis.
Analytics & ROI Dashboard showing 2.8M total transactions, €4.52M fraud blocked, 96.8% detection accuracy, 2847% ROI, ensemble ML model performance metrics, top risk features, fraud geographic distribution, and real-time system performance.
AI Agents
Specialized autonomous agents working in coordination
Orchestrator Agent
Fraud investigations require coordinating multiple specialized analysis pipelines with strict latency requirements while managing complex dependencies between agents.
Core Logic
Uses GPT-4 Turbo with LangGraph DAG for workflow management. Coordinates all 8 agents, initializes investigation pipelines, configures agent interactions, manages parallel and sequential processing phases, and synthesizes final investigation results within sub-second latency requirements.
Feature Engineer Agent
Fraud detection requires computing hundreds of features from multiple data sources in real-time, including transaction history, device data, and behavioral patterns.
Core Logic
Computes 42 features from Feature Store (Feast/Tecton pattern) with <200ms P99 latency. Processes 8 real-time features (transaction amount, IP reputation, device risk), 10 historical features (avg_30d, velocity_1h), and 8 derived features (amount_deviation_ratio, risk_composite) for downstream model consumption.
Pattern Detector Agent
Fraudulent transactions exhibit subtle anomalies in amount, timing, and merchant patterns that differ from legitimate customer behavior.
Core Logic
Combines Isolation Forest with LSTM neural networks running on GPU A100. Detects amount deviations from customer baseline, timing anomalies relative to historical patterns, and merchant category shifts. Outputs anomaly scores with confidence levels and specific anomaly type classifications.
Behavioral Analyzer Agent
Account takeover and synthetic identity fraud exhibit behavioral signatures that require contextual understanding beyond numerical patterns.
Core Logic
Powered by Claude 3.5 Sonnet LLM to analyze user behavior and account activity patterns. Detects geographic shifts, device consistency issues, session behavior anomalies, and usage pattern deviations. Provides AI-generated natural language behavioral insights explaining detected anomalies.
Network Graph Agent
Fraud rings involve multiple connected accounts that share devices, addresses, or transaction patterns. Individual transaction analysis misses these network-level fraud patterns.
Core Logic
Uses Graph Neural Networks to investigate fraud rings and network connections. Builds connection graphs linking accounts by shared attributes, calculates fraud ring probability scores, identifies flagged connections to known fraudsters, and traces multi-hop relationships in the fraud network.
Risk Scorer Agent
Single models have blind spots. Combining multiple models with different strengths improves overall fraud detection while reducing false positives.
Core Logic
Runs parallel inference on XGBoost v3.2 (AUC: 0.973), Neural Network v2.1 (AUC: 0.969), Random Forest v1.8 (AUC: 0.958), and Rule Engine v4.0 (Precision: 0.945). Combines outputs using F1-weighted ensemble averaging to produce a composite risk score with calibrated confidence intervals.
Explainability Engine Agent
Regulatory requirements (GDPR right to explanation) and analyst trust require understanding why a transaction was flagged, not just the risk score.
Core Logic
Generates SHAP values and LIME explanations for every feature's contribution to the risk score. Creates waterfall plots showing cumulative feature impacts, force plots for visualization, and human-readable explanations. Provides counterfactual analysis showing what would need to change to alter the decision.
Decision Maker Agent
Final fraud decisions must balance false positive costs against fraud losses while considering customer experience and regulatory requirements.
Core Logic
Combines Policy Engine rules with LLM reasoning to make final fraud decisions (APPROVE, CHALLENGE, BLOCK, ESCALATE). Evaluates alternative actions and trade-offs, determines appropriate challenge methods (SMS, email, biometric), generates customer communication messages, and ensures PCI DSS and PSD2 compliance.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
11 technologies
Architecture Diagram
System flow visualization