Transaction flow networks are crucial in detecting illicit activities such as wash trading, credit card fraud, cashback arbitrage fraud, and money laundering. \revise{Our collaborator, Grab, a leader in digital payments in Southeast Asia, faces increasingly sophisticated fraud patterns in its transaction flow networks. In industry settings such as Grab's fraud detection pipeline, identifying fraudulent activities heavily relies on detecting dense flows within transaction networks. Motivated by this practical foundation,} we propose the \emph{\(S\)-\(T\) densest flow} (\SDMF{}) query. Given a transaction flow network \( G \), a source set \( \Src \), a sink set \( \Dst \), and a size threshold \( k \), the query outputs subsets \( \Src' \subseteq \Src \) and \( \Dst' \subseteq \Dst \) such that the maximum flow from \( \Src' \) to \( \Dst' \) is densest, with \(|\Src' \cup \Dst'| \geq k\). Recognizing the NP-hardness of the \SDMF{} query, we develop an efficient divide-and-conquer algorithm, CONAN. \revise{Driven by industry needs for scalable and efficient solutions}, we introduce an approximate flow-peeling algorithm to optimize the performance of CONAN, enhancing its efficiency in processing large transaction networks. \revise{Our approach has been integrated into Grab's fraud detection scenario, resulting in significant improvements in identifying fraudulent activities.} Experiments show that CONAN outperforms baseline methods by up to three orders of magnitude in runtime and more effectively identifies the densest flows. We showcase CONAN's applications in fraud detection on transaction flow networks from our industry partner, Grab, and on non-fungible tokens (NFTs).

翻译：交易流网络在检测诸如刷单交易、信用卡欺诈、返现套利欺诈和洗钱等非法活动中至关重要。我们的合作方，东南亚数字支付领域的领导者Grab，在其交易流网络中面临着日益复杂的欺诈模式。在诸如Grab欺诈检测流水线等工业场景中，识别欺诈活动在很大程度上依赖于检测交易网络中的密集流。基于这一实际背景，我们提出了\emph{\(S\)-\(T\)最密流}查询。给定一个交易流网络\( G \)、一个源点集合\( \Src \)、一个汇点集合\( \Dst \)以及一个规模阈值\( k \)，该查询输出子集\( \Src' \subseteq \Src \)和\( \Dst' \subseteq \Dst \)，使得从\( \Src' \)到\( \Dst' \)的最大流是最密的，且满足\(|\Src' \cup \Dst'| \geq k\)。认识到\SDMF{}查询的NP难性质，我们开发了一种高效的分治算法CONAN。受业界对可扩展高效解决方案需求的驱动，我们引入了一种近似流剥离算法来优化CONAN的性能，从而提升其处理大型交易网络的效率。我们的方法已集成到Grab的欺诈检测场景中，在识别欺诈活动方面取得了显著改进。实验表明，CONAN在运行时间上比基线方法快多达三个数量级，并且能更有效地识别最密流。我们展示了CONAN在来自我们行业合作伙伴Grab的交易流网络以及非同质化代币上的欺诈检测应用。