Abstract
The rise of digital ecosystems has exposed the financial sector to evolving abuse and criminal tactics that share operational knowledge and techniques both within and across different environments (fiat-based, crypto-assets, etc.). Traditional rule-based systems lack the adaptability needed to detect sophisticated or coordinated criminal behaviors (patterns), highlighting the need for strategies that analyze actors' interactions to uncover suspicious activities and extract their modus operandi. For this reason, in this work, we propose an approach that integrates graph machine learning and network analysis to improve the detection of well-known topological patterns within transactional graphs. However, a key challenge lies in the limitations of traditional financial datasets, which often provide sparse, unlabeled information that is difficult to use for graph-based pattern analysis. Therefore, we firstly propose a four-step preprocessing framework that involves (i) extracting graph structures, (ii) considering data temporality to manage large node sets, (iii) detecting communities within, and (iv) applying automatic labeling strategies to generate weak ground-truth labels. Then, once the data is processed, Graph Autoencoders are implemented to distinguish among the well-known topological patterns. Specifically, three different GAE variants are implemented and compared in this analysis. Preliminary results show that this pattern-focused, topology-driven method is effective for detecting complex financial crime schemes, offering a promising alternative to conventional rule-based detection systems.
Abstract (translated)
数字生态系统的兴起使金融行业暴露于不断演变的滥用和犯罪策略之下，这些策略在不同的环境（如法定货币、加密资产等）之间共享操作知识和技术。传统的基于规则的系统缺乏检测复杂或协同犯罪行为所需的适应性，强调了分析参与者互动以揭露可疑活动并提取其作案手法的战略需求。为此，在这项工作中，我们提出了一种结合图机器学习和网络分析的方法，旨在改进在交易图中识别已知拓扑模式的能力。 然而，关键挑战在于传统金融数据集的局限性：这些数据集通常提供稀疏且未标记的信息，难以用于基于图的模式分析。因此，我们首先提出了一个四步预处理框架，包括(i)提取图结构；(ii)考虑数据的时间性来管理大型节点集合；(iii)检测内部社区；以及(iv)采用自动标注策略生成弱事实标签。 一旦数据经过处理，我们将实施图自编码器（Graph Autoencoders）以区分已知的拓扑模式。具体而言，在这项分析中实现了并比较了三种不同的GAE变体。初步结果显示，这种方法专注于模式、驱动于拓扑的方法在检测复杂的金融犯罪方案方面是有效的，并为传统的基于规则的检测系统提供了一个有希望的替代选择。
URL
https://arxiv.org/abs/2509.12730
