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 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".

Efficient GPU Implementation for Single Block Orthogonal Dictionary Learning

Dictionary training for sparse representations involves dealing with large chunks of data and complex algorithms that determine time consuming implementations. SBO is an iterative dictionary learning algorithm based on constructing unions of orthonormal bases via singular value decomposition, that represents each data item through a single best fit orthobase. In this paper we present a GPGPU approach of implementing SBO in OpenCL. We provide a lock-free solution that ensures full-occupancy of the GPU by following the map-reduce model for the sparse-coding stage and by making use of the Partitioned Global Address Space (PGAS) model for developing parallel dictionary updates. The resulting implementation achieves a favourable trade-off between algorithm complexity and data representation quality compared to PAK-SVD which is the standard overcomplete dictionary learning approach. We present and discuss numerical results showing a significant acceleration of the execution time for the dictionary learning process.