In recent years, the digitization and automation of anti-financial crime (AFC) investigative processes have faced significant challenges, particularly the need for interpretability of AI model results and the lack of labeled data for training. Network analysis has emerged as a valuable approach in this context. In this paper, we present WeirdFlows, a top-down search pipeline for detecting potentially fraudulent transactions and non-compliant agents. In a transaction network, fraud attempts are often based on complex transaction patterns that change over time to avoid detection. The WeirdFlows pipeline requires neither an a priori set of patterns nor a training set. In addition, by providing elements to explain the anomalies found, it facilitates and supports the work of an AFC analyst. We evaluate WeirdFlows on a dataset from Intesa Sanpaolo (ISP) bank, comprising 80 million cross-country transactions over 15 months, benchmarking our implementation of the algorithm. The results, corroborated by ISP AFC experts, highlight its effectiveness in identifying suspicious transactions and actors, particularly in the context of the economic sanctions imposed in the EU after February 2022. This demonstrates \textit{WeirdFlows}' capability to handle large datasets, detect complex transaction patterns, and provide the necessary interpretability for formal AFC investigations.

翻译：近年来，反金融犯罪（AFC）调查流程的数字化与自动化面临重大挑战，特别是AI模型结果的可解释性需求以及训练用标注数据的缺乏。在此背景下，网络分析已成为一种有价值的研究方法。本文提出WeirdFlows，一种用于检测潜在欺诈交易与违规主体的自上而下搜索流程。在交易网络中，欺诈企图通常基于随时间变化以规避检测的复杂交易模式。WeirdFlows流程既不需要预设模式集合，也无需训练数据集。此外，通过提供解释所发现异常的依据，该流程能够协助并支持AFC分析师的工作。我们在Intesa Sanpaolo（ISP）银行包含15个月内8000万笔跨境交易的数据集上评估WeirdFlows，并对算法实现进行基准测试。经ISP银行AFC专家验证的结果表明，该方法在识别可疑交易与行为主体方面具有显著效果，特别是在2022年2月后欧盟实施经济制裁的背景下。这证明了\textit{WeirdFlows}能够处理大规模数据集、检测复杂交易模式，并为正式的AFC调查提供必要的可解释性。