Money Laundering Detection with Node2Vec

Abstract

Highlights• This paper focuses on money laundering detections with machine learning. • A graph-based representation for banking transactions is used.• The proposed Node2Vec based solution is analyzed on a dataset.

A systematic review of anti-money laundering systems literature: Exploring the efficacy of machine learning and deep learning integration

“…Random Forest, Support Vector Machine, and Gradient Boosted Tree each appeared three times, showing their prevalence and effectiveness in dealing with complex financial data (Alkhalili et al, 2021;Alotibi et al, 2022;Labanca et al, 2022;Masrom et al, 2023;Ruiz & Angelis, 2022;Pocher et al, 2022;Ruchay et al, 2023;Zhang & Trubey, 2019) . Algorithms such as K-Nearest Neighbors, Naive Bayes, Node2Vec, Reinforcement Learning, Deep Neural Network, Isolation Forest, and Deep Protect each appear once, providing diverse tools used to tackle money laundering and financial crime (Alkhalili et al, 2021;Alotibi et al, 2022;Caglayan & Bahtiyar, 2022;Labanca et al, 2022;Lopes et al, 2022;Shahbazi & Byun, 2022). All of these algorithms collaborate to help improve the ability to detect and prevent increasingly complex money laundering.…”

“…Especially in graphical models, the detection process turns out to be more difficult because numerous techniques are implicated in money laundering (Caglayan & Bahtiyar, 2022).…”

“…Unfortunately, the influence of data quality is frequently underestimated because the focus on developing learning model performance is overly intense. However, both data quality and model quality should be carefully considered when creating an efficient solution to tackle money laundering (Caglayan & Bahtiyar, 2022;Gupta et al, 2022).…”

Fraud Detection and Anti-Money Laundering Applying Machine Learning Techniques in Cryptocurrency Transactional Graphs

“…Geng et al (2022) proposed an enhanced version of GCN, a graph attention mechanism, with the potential for detecting illegal activities in blockchain networks, demonstrating the usefulness of graph learning in addressing blockchain system security. Caglayan and Bahtiyar (2022) proposed a graph embedding algorithm called node2vec to detect money laundering in financial transactions. It proved to be superior to other methods for detecting money laundering in a banking transaction dataset.…”