Building an Agentic Fraud Investigation System with TigerGraph and 11 AI Agents

The article describes the development of an "Agentic Fraud Investigation System" using the TigerGraph graph database and 11 specialized AI agents.

The system connects a suspicious transaction to various entities such as customers, cards, devices, email domains, billing regions and previous fraud cases through relationships stored in a graph database. This allows analysts to explore the broader network of transactions and connections rather than just looking at isolated records.

Key points:

1. The system starts with a suspicious transaction and creates an investigation case. The analyst interacts with it through an AI chat interface to retrieve information, analyze graph relationships and receive recommendations.

2. Traditional fraud analysis looks at individual transactions, but fraud often involves networks of related entities. By modeling these relationships in a graph, the system can identify patterns and evidence that would be missed otherwise. For example, the same device may be used by multiple customers, cards connected to the same device, accounts linked to confirmed fraud cases, etc.

3. The architecture consists of a React UI for presenting the investigation, an agent layer to handle reasoning and task execution, TigerGraph for the graph database and evidence layer, an LLM for the natural language interface, and the agents themselves.

4. The investigation workflow involves creating a case, collecting relevant information, exploring graph relationships, checking previous cases, identifying fraud patterns, evaluating evidence, determining if more evidence is needed, recommending next actions, and saving the investigation result.

5. The 11 AI agents take on specialized roles such as investigation coordinator, graph analysis, transaction analysis, fraud analysis, evidence analysis, case analysis, etc. This multi-agent approach enables a more comprehensive investigation workflow.

In summary, the article outlines a graph-based approach to fraud investigation that leverages TigerGraph, AI agents, and natural language processing to move beyond simple risk scores and help analysts uncover deeper connections and evidence to guide actionable decisions.