Extracting Money Laundering Transactions from Quasi-Temporal Graph Representation

TL;DR

This paper introduces ExSTraQt, a scalable supervised learning framework that improves money laundering transaction detection accuracy using quasi-temporal graph representations, outperforming existing models.

Contribution

The paper presents a novel, simple, and scalable supervised learning approach for AML detection that enhances accuracy and integrates seamlessly with existing systems.

Findings

Achieved up to 1% F1 score improvement on real datasets.

Improved detection accuracy by over 8% on synthetic datasets.

Framework is scalable with low computational and memory requirements.

Abstract

Money laundering presents a persistent challenge for financial institutions worldwide, while criminal organizations constantly evolve their tactics to bypass detection systems. Traditional anti-money laundering approaches mainly rely on predefined risk-based rules, leading to resource-intensive investigations and high numbers of false positive alerts. In order to restrict operational costs from exploding, while billions of transactions are being processed every day, financial institutions are investing in more sophisticated mechanisms to improve existing systems. In this paper, we present ExSTraQt (EXtract Suspicious TRAnsactions from Quasi-Temporal graph representation), an advanced supervised learning approach to detect money laundering (or suspicious) transactions in financial datasets. Our proposed framework excels in performance, when compared to the state-of-the-art AML (Anti Money Laundering) detection models. The key strengths of our framework are sheer simplicity, in terms of design and number of parameters; and scalability, in terms of the computing and memory requirements. We evaluated our framework on transaction-level detection accuracy using a real dataset; and a set of synthetic financial transaction datasets. We consistently achieve an uplift in the F1 score for most datasets, up to 1% for the real dataset; and more than 8% for one of the synthetic datasets. We also claim that our framework could seamlessly complement existing AML detection systems in banks. Our code and datasets are available at https://github.com/mhaseebtariq/exstraqt.