FlowSeries: Anomaly Detection in Financial Transaction Flows

Abstract

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.

12 pages, 6 figures, ITADATA2024

References in corpus (6)

• Using social network analysis to prevent money laundering
• An expert system for detecting automobile insurance fraud using social network analysis
• Modeling and Understanding Ethereum Transaction Records via a Complex Network Approach
• Specialization of strategies and herding behavior of trading firms in a financial market
• Cryptocurrencies Activity as a Complex Network: Analysis of Transactions Graphs
• Deep Set Classifier for Financial Forensics: An application to detect money laundering