Financial fraud targeting elderly individuals is a critical issue, with the FBI's Internet Crime Complaint Center (IC3) reporting that seniors lost over $3.4 billion to fraud in 2023 alone, marking an 11% increase from the previous year. This alarming figure underscores the vulnerability of the elderly to sophisticated scams and organized fraud rings that exploit trust and lack of technological familiarity.
With nearly 101,068 complaints filed by those over 60, and an average loss of $33,915 per complaint, it is evident that traditional fraud detection systems often fall short in protecting this demographic.
Inspired by these challenges and the potential of advanced technologies, we aim to develop an agentic financial fraud ring detection system. By leveraging GraphRAG for complex network analysis, AI agents for autonomous pattern recognition, and GPU-accelerated analytics for real-time processing, our goal is to identify influential fraudsters, uncover hidden fraud networks, and provide financial institutions with the tools needed to protect the elderly and ensure financial security.
Our AI-powered system detects fraud rings by analyzing transactional relationships using graph-based AI and RAG (Retrieval-Augmented Generation). It:
🔹 Builds a fraud network graph from financial transactions.
🔹 Identifies influential fraudsters using PageRank and centrality measures.
🔹 Detects hidden fraud rings using GraphRAG and GPU-accelerated graph traversal.
🔹 Answers complex fraud queries dynamically using AI agents.
🔹 Generates explainable fraud insights to assist financial analysts.
🚀 Tech Stack & Methodologies
1️⃣ Graph-based Fraud Detection: We used ArangoDB as a multi-model graph database to represent transactions and fraud rings.
2️⃣ Agentic Query Processing: Built an AI agent using LangChain + NVIDIA cuGraph to classify queries and retrieve fraud insights dynamically.
3️⃣ GraphRAG for Enhanced Retrieval: Integrated GraphRAG to retrieve fraud evidence efficiently using context-aware AI.
4️⃣ GPU-Accelerated Graph Analytics: Leveraged NVIDIA cuGraph to speed up fraud detection using parallel processing.
5️⃣ Hybrid Query Execution: Combined AQL (ArangoDB Query Language) with NetworkX to handle both structured (graph traversal) and unstructured (fraud influence analysis) queries.
6️⃣ Explainability with LLMs: Used langchain-groq to generate natural language fraud explanations from graph-based insights.
🔸 Data Availability – Finding a realistic financial fraud dataset was difficult, so we experimented with synthetic and open datasets.
🔸 Hybrid Query Execution Complexity – Ensuring smooth integration between AQL-based fraud retrieval and NetworkX-based fraud influence analysis was challenging.
🔸 Dynamic AI Agent Reliability – The agent sometimes misclassified hybrid queries, requiring better prompt engineering and tool selection logic.
✅ Built an AI agent that dynamically processes fraud-related queries
✅ Successfully integrated GraphRAG for intelligent retrieval of fraud evidence
✅ Implemented GPU-accelerated graph analytics to detect fraud rings efficiently
✅ Developed an explainable AI system that generates natural language fraud insights
📌 Graph-based fraud detection is highly effective for uncovering hidden fraud rings.
📌 Hybrid query execution (AQL + NetworkX) enables deeper fraud insights.
📌 AI agents need robust prompt engineering to correctly classify and execute queries.
🔹 Real-world dataset integration with banking and financial institutions.
🔹 Expanding multi-agent capabilities to handle real-time fraud case investigations.