Knowledge Graph-Enhanced Machine Learning for Financial Crime Intelligence: Linking Fraud, Money Laundering, and Cybersecurity Threats Across Distributed Ledgers

Abstract

Financial crime in digital asset ecosystems has become a lot harder to track lately. The bad actors are using complicated, overlapping networks of transactions that standard machine learning methods just cannot detect accurately. This paper looks into a graph-based learning setup to identify this kind of illegal activity in cryptocurrency networks by focusing entirely on the connections built into the financial data. The experiment uses the Elliptic Bitcoin Dataset, which is a massive network of labeled transactions split into legal, illegal, and unknown categories. The setup treats the money flows as a directed graph that changes over time, turning individual transactions into points and the actual Bitcoin transfers into the lines connecting them. The approach combines a few different ideas. It starts with basic machine learning models trained on custom transaction details, adds in graph embedding tricks like Node2Vec, and finishes with graph neural networks, specifically GraphSAGE, to handle the semi-supervised sorting of the data points. To keep the testing fair and realistic, the data was split strictly by time. That stops information from leaking out of the future, mimicking a real fraud detection setup where an analyst has to guess what comes next based only on what happened in the past. Success is measured using precision, recall, F1-score, ROC-AUC, and ranking tricks like Precision@K. These metrics are necessary because the dataset is incredibly lopsided, which is always the case with financial fraud, where the bad transactions are buried in a sea of normal ones. On top of that, structural clues from the graph shapes and extra explanation tools were added to make the caught illegal patterns easier to understand, giving financial investigators something useful to actually work with. The main goal here is to show that paying attention to the shape of the graph does a much better job of spotting fraudsters than just looking at standard flat tables. It also helps uncover hidden groups of transactions that point to people working together. The results help push forward better graph-based crime-fighting tools that can handle the structural risks built into decentralized money networks.