FlowSeries: Anomaly Detection in Financial Transaction Flows

In recent years, the digitization and automation of anti-financial crime (AFC) investigative processes have faced significant chal-lenges, 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 FlowSeries, 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 FlowSeries 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 FlowSeries on a dataset from Intesa Sanpaolo (ISP) bank, comprising 80 million cross-country transactions over 15 months, bench-marking 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 sanc-tions imposed in EU after February 2022. This demonstrates FlowSeries’ capability to handle large datasets, detect complex transaction patterns, and provide the necessary interpretability for formal AFC investigations.