project 03
Agentic Fraud Analysis
A three-agent architecture that handles fraud incidents end-to-end: Alert Triage Agent consolidates alerts and creates incidents, Diagnose Agent performs root cause analysis, and Mitigation Agent develops and deploys automated solutions.
Python · MCP · Multi-Agent · LLM · FastAPI · React
The problem
Fraud investigation requires analyzing multiple data sources, correlating patterns across alerts, and synthesizing findings - a time-consuming manual process that delays response time.
The approach
Built a three-agent system where specialized agents handle different aspects of fraud investigation: Alert Triage for consolidation, Diagnose for RCA, and Mitigation for automated fixes. Orchestrated through MCP (Model Context Protocol) tool calls with human-in-the-loop approval.
The outcome
Automated significant portions of fraud investigation workflow with consistent, auditable analysis trails and faster incident response.
Architecture
Key features
- Three-agent architecture: Alert Triage, Diagnose, and Mitigation agents
- MCP servers for modular tool capabilities (data analysis, ML models)
- AI Proxy for request routing and authentication
- Human-in-the-loop UI for reviewing and approving agent plans
- Automated alert correlation and incident creation
- Root cause analysis with attack pattern identification