图特征预处理器：面向金融犯罪检测的基于子图的实时特征提取
Graph Feature Preprocessor: Real-time Subgraph-based Feature Extraction for Financial Crime Detection
AI总结：
本文提出图特征预处理器，通过实时挖掘交易图中的洗钱子图模式生成丰富特征，结合梯度提升模型，在反洗钱和钓鱼检测中以更高少数类F1分数和吞吐率超越图神经网络基线。
摘要：
本文提出了“图特征预处理器”（Graph Feature Preprocessor），这是一个用于实时检测金融交易图中典型洗钱模式的软件库。这些模式被用来生成丰富的交易特征，以服务于下游机器学习训练和推理任务，例如欺诈性金融交易检测。我们表明，经过丰富化的交易特征能够显著提升基于梯度提升的机器学习模型的预测准确率。该库利用多核并行能力，维护动态内存图，并对流入的交易流高效挖掘子图模式，从而支持流式运行方式。我们的解决方案将图特征预处理器与基于梯度提升的机器学习模型相结合，在反洗钱和钓鱼检测数据集上，能够以比标准图神经网络更高的少数类 F1 分数检测非法交易。此外，在多核 CPU 上执行的端到端吞吐率优于在强大的 V100 GPU 上运行的图神经网络基线。总体而言，高准确率、高吞吐率和低延迟的结合证明了该库在实际应用中的实用价值。
英文摘要：
In this paper, we present "Graph Feature Preprocessor", a software library for detecting typical money laundering 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 detection of fraudulent financial transactions. 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. Our solution, which combines our Graph Feature Preprocessor and gradient-boosting-based machine learning models, can detect illicit transactions with higher minority-class F1 scores than standard graph neural networks in anti-money laundering and phishing datasets. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
