[2604.01315] Detecting Complex Money Laundering Patterns with Incremental and Distributed Graph Modeling

Computer Science > Machine Learning arXiv:2604.01315 (cs) [Submitted on 1 Apr 2026] Title:Detecting Complex Money Laundering Patterns with Incremental and Distributed Graph Modeling Authors:Haseeb Tariq, Alen Kaja, Marwan Hassani View a PDF of the paper titled Detecting Complex Money Laundering Patterns with Incremental and Distributed Graph Modeling, by Haseeb Tariq and 1 other authors View PDF HTML (experimental) Abstract:Money launderers take advantage of limitations in existing detection approaches by hiding their financial footprints in a deceitful manner. They manage this by replicating transaction patterns that the monitoring systems cannot easily distinguish. As a result, criminally gained assets are pushed into legitimate financial channels without drawing attention. Algorithms developed to monitor money flows often struggle with scale and complexity. The difficulty of identifying such activities is further intensified by the (persistent) inability of current solutions to control the excessive number of false positive signals produced by rigid, risk-based rules systems. We propose a framework called ReDiRect (REduce, DIstribute, and RECTify), specifically designed to overcome these challenges. The primary contribution of our work is a novel framing of this problem in an unsupervised setting; where a large transaction graph is fuzzily partitioned into smaller, manageable components to enable fast processing in a distributed manner. In addition, we define a refined ...