# Detecting Complex Money Laundering Patterns with Incremental and Distributed Graph Modeling

arXiv: https://arxiv.org/abs/2604.01315 · HTML: https://mlsanity.com/paper/2604.01315/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 1 Apr 2026.

## Builds on (most cited first, 14 of 14)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [Graph-based Anomaly Detection and Description: A Survey](https://mlsanity.com/paper/1404.4679/) (2014)
- [Realistic Synthetic Financial Transactions for Anti-Money Laundering Models](https://mlsanity.com/paper/2306.16424/) (2023)
- [Topology-Agnostic Detection of Temporal Money Laundering Flows in Billion-Scale Transactions](https://mlsanity.com/paper/2309.13662/) (2023)
- [Detection of money laundering groups using supervised learning in networks](https://mlsanity.com/paper/1608.00708/) (2016)
- [FAST-PPR: Scaling Personalized PageRank Estimation for Large Graphs](https://mlsanity.com/paper/1404.3181/) (2014)
- [CubeFlow: Money Laundering Detection with Coupled Tensors](https://mlsanity.com/paper/2103.12411/) (2021)
- [Graph Feature Preprocessor: Real-time Subgraph-based Feature Extraction for Financial Crime Detection](https://mlsanity.com/paper/2402.08593/) (2024)
- [Size Matters: A Comparative Analysis of Community Detection Algorithms](https://mlsanity.com/paper/1712.01690/) (2017)
- [MonLAD: Money Laundering Agents Detection in Transaction Streams](https://mlsanity.com/paper/2201.10051/) (2022)
- [Privacy-Enhancing Technologies for Financial Data Sharing](https://mlsanity.com/paper/2306.10200/) (2023)

## Built on by (most cited first, 1 of 1)

- [Extracting Money Laundering Transactions from Quasi-Temporal Graph Representation](https://mlsanity.com/paper/2604.02899/) (2026)

## Abstract (arXiv)

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 evaluation metric that better captures the effectiveness of exposed money laundering patterns. Through comprehensive experimentation, we demonstrate that our framework achieves superior performance compared to existing and state-of-the-art techniques, particularly in terms of efficiency and real-world applicability. For validation, we used the real (open source) Libra dataset and the recently released synthetic datasets by IBM Watson. Our code and datasets are available at https://github.com/mhaseebtariq/redirect.