Graph Feature Preprocessor: Real-time Extraction of Subgraph-based Features from Transaction Graphs

In this paper, we present "Graph Feature Preprocessor", a software library for detecting typical money laundering and fraud patterns in financial transaction graphs in real time. These patterns are used to produce a rich set of transaction features for downstream machine learning training and inference tasks such as money laundering detection. We show that our enriched transaction features dramatically improve the prediction accuracy of gradient-boosting-based machine learning models. Our library exploits multicore parallelism, maintains a dynamic in-memory graph, and efficiently …

cs.ai cs.lg extraction feature features financial fraud graph graphs inference library machine machine learning money paper patterns preprocessor real-time set software tasks training