Mature Practices for Building Risk‑Control Knowledge Graphs on NebulaGraph and Leveraging Large Language Models

In the current data‑driven era, graph databases and large language models (LLMs) like GPT are increasingly integrated to provide intelligent and flexible solutions for complex business scenarios, especially in risk‑control where real‑time response and personalized decision support are critical.

NebulaGraph, an open‑source ultra‑large‑scale graph database with millisecond latency and distributed architecture, is used to build a billion‑node fraud‑detection graph that supports both real‑time transaction monitoring and offline analytical tasks such as community detection, path analysis, and behavior‑pattern recognition.

LLMs can extend graph capabilities through several techniques: converting natural‑language questions to GQL (Text‑to‑GQL), enabling agents to understand serialized graph data, constructing an “exploration chain” that iteratively plans and executes graph queries, and transforming unstructured data into semi‑structured knowledge graphs via triple extraction and hierarchical management.

The NebulaGraphRAG platform and its developer SDK provide a graph‑based QA system that combines structured graph queries with LLM reasoning, offering richer information processing and paving the way for future agent‑driven rule automation and intelligent analysis in risk‑control.

The Q&A session covered topics such as integrating LLM/Agent features into front‑end explorer tools, cost‑benefit considerations of GraphRAG versus traditional vector embeddings, and strategies like lazy‑loading to reduce the expense of generating multiple community reports.