AI-Assisted Fraud Analysis System Integrating RAG and Knowledge Graph

This project was developed by JavierHerasJimenez and open-sourced on GitHub (link: https://github.com/JavierHerasJimenez/Fraud-Analysis-RAG-Knowledge-Graph, release date: 2026-06-03). The project integrates Retrieval-Augmented Generation (RAG), Large Language Models (LLM), and knowledge graph technologies to build an AI-assisted fraud detection system, addressing the limitations of traditional fraud detection methods in dealing with complex tactics. Its core architecture includes an RAG layer (retrieving relevant information), an LLM layer (understanding queries and generating conclusions), and a knowledge graph layer (capturing entity relationships and multi-hop reasoning). It can be applied in fields such as insurance, finance, and corporate investigations, providing a reference for the application of AI in risk management and compliance.

Traditional fraud detection relies on rule engines and statistical models, which struggle to handle complex fraud tactics (multi-entity, cross-document, unstructured data). The rise of LLM and RAG technologies brings new possibilities, but LLMs have hallucination and context limitations, while pure RAG struggles to capture entity relationships; knowledge graphs can structure entity relationships and build queryable and reasoning knowledge networks, providing ideas for solving these problems.

The core of the project is a collaborative pipeline integrating RAG, LLM, and knowledge graph:

1. RAG layer: Vector retrieval quickly locates relevant document fragments, expands the knowledge boundary of LLMs, and reduces the risk of hallucinations;
2. LLM layer: Understands natural language queries and synthesizes information to generate analysis conclusions;
3. Knowledge graph layer: Extracts entities (people, companies, etc.) and relationships, supporting multi-hop reasoning (e.g., tracking fund flows). Key technologies include: document parsing and information extraction (OCR, NER, relation extraction), hybrid retrieval strategy (vector + keyword), knowledge graph exploration and reasoning, and multi-round interactive analysis.

• Insurance fraud detection: Analyze claim documents to identify abnormal patterns (e.g., suspicious doctors/clinics);
• Financial transaction monitoring: Discover associated account networks and identify money laundering behavior characteristics;
• Corporate internal investigations: Reconstruct event timelines and identify key figures and communication patterns;
• Compliance review automation: Automate KYC/AML document analysis and mark anomalies.

Infrastructure requirements: Vector databases (Pinecone, Weaviate, etc.) to support semantic retrieval; graph databases (Neo4j, etc.) to store and query knowledge graphs; LLMs can choose open-source (Llama) or commercial APIs (GPT-4). Security and privacy: Local deployment is required to ensure sensitive documents do not leave the country; fine-grained access control should be implemented to ensure authorized data access.

The integration of RAG, LLM, and knowledge graph addresses the limitations of traditional methods, produces synergistic effects, and provides new possibilities for fraud detection. The project demonstrates a systematic AI application approach (collaboration between LLM and other technologies), providing a reference for practitioners in risk management and compliance, and representing the evolutionary direction of AI in complex analysis tasks.