Overview
Traditional fraud detection relies on static rules and batch-scoring models that flag transactions after the fact. Agentic AI changes the paradigm: agents operate in a continuous perceive-act loop, ingesting streaming events, querying contextual data sources, and making real-time interdiction decisions — often before a transaction settles.
Our research focuses on building multi-agent fraud detection systems that combine graph-based relationship modeling, behavioral analytics, and LLM-driven reasoning to surface novel fraud patterns that rule-based systems miss.
How Agents Detect Fraud
Each transaction triggers an agent that enriches the event with entity context, traverses a transaction graph for risk signals, applies behavioral models, and issues a risk verdict — all within the transaction window.
Streaming Event Ingestion
Agents subscribe to Kafka or Pulsar topics, consuming payment events, login attempts, and device signals in real time with sub-10ms processing latency.
Entity Context Enrichment
Each event is enriched with account history, device fingerprint, geolocation, and network relationships pulled from the AI Data Lakehouse.
Graph Anomaly Detection
A Neo4J transaction graph surfaces ring-fraud patterns, mule account networks, and velocity anomalies that are invisible in flat feature tables.
LLM-Driven Case Reasoning
For borderline cases, an LLM agent synthesizes enriched context into a human-readable risk narrative and escalation recommendation.
Multi-Agent Pipeline
Our fraud detection architecture is composed of specialized agents with distinct roles:
- Triage Agent — First-pass scoring agent that classifies incoming events into risk tiers and routes them to appropriate specialist agents.
- Identity Agent — Validates account ownership, device binding, and behavioral biometrics to detect account takeover and synthetic identity fraud.
- Network Agent — Traverses the transaction graph to identify coordinated fraud rings, shared infrastructure, and money mule pathways.
- Decision Agent — Aggregates signals from all upstream agents, applies policy rules, and issues a final allow / challenge / decline verdict with an audit log.
Applications
Collaborate
Building a real-time fraud platform?
We partner with financial institutions and fintechs to design agentic fraud detection systems that operate at transaction speed.
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