  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 mines
subgraph patterns in the incoming transaction stream, which enables it to be
operated in a streaming manner. We evaluate our library using highly-imbalanced
synthetic anti-money laundering (AML) and real-life Ethereum phishing datasets.
In these datasets, the proportion of illicit transactions is very small, which
makes the learning process challenging. Our solution, which combines our Graph
Feature Preprocessor and gradient-boosting-based machine learning models, is
able to detect these illicit transactions with higher minority-class F1 scores
than standard graph neural networks. In addition, the end-to-end throughput
rate of our solution executed on a multicore CPU outperforms the graph neural
network baselines executed on a powerful V100 GPU. Overall, the combination of
high accuracy, a high throughput rate, and low latency of our solution
demonstrates the practical value of our library in real-world applications.
Graph Feature Preprocessor has been integrated into IBM mainframe software
products, namely "IBM Cloud Pak for Data on Z" and "AI Toolkit for IBM Z and
LinuxONE".
