GraphRAG in Action: A Simple Agent for Know-Your-Customer Investigations
Blog post from Neo4j
In the realm of financial services, Know-Your-Customer (KYC) and Anti-Money Laundering (AML) are critical elements for combating illicit activities, and this blog post details the implementation of a KYC agent using OpenAI’s Agents SDK, MCP, Neo4j, and Ollama. The KYC agent is designed to navigate complex networks of customer relationships using a knowledge graph, allowing it to identify potential fraud patterns and suspicious activities through interconnected data points like customers, accounts, transactions, and devices. By leveraging graph-powered data retrieval tools, the agent can perform tasks such as detecting circular transaction patterns, retrieving customer details, and generating Cypher queries from natural language inputs. This approach offers a robust framework for dynamic querying and persistent memory storage, enabling the agent to evolve its knowledge base and assist in fraud investigations effectively. The blog also highlights the potential application of these patterns and tools beyond KYC, in domains like supply chain analysis and drug discovery, emphasizing the growing relevance of graph-aware AI agents.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| MCP | 38 | 3,840 | 275 | 112 | +19% |
| RAG | 5 | 1,187 | 205 | 87 | +21% |
| AI Agents | 3 | 2,479 | 485 | 152 | +12% |
| LLM | 3 | 3,922 | 600 | 189 | -6% |
| AI Model Fine-tuning | 1 | 568 | 107 | 59 | -14% |
| Observability | 1 | 1,883 | 347 | 119 | -9% |
| Vector Search | 1 | 1,678 | 256 | 103 | -9% |
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