Financial Crime World

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Recent Advances in Money Laundering and Financial Crimes Research

This article presents a curated list of recent academic papers related to money laundering and financial crimes. The papers are organized into categories, including Money Laundering Detection, Illicit Financial Flows, and Graph Neural Networks in Financial Applications.

Money Laundering Detection

  • Inspection-L: Self-Supervised GNN Node Embeddings for Money Laundering Detection in Bitcoin
    • Wai Weng Lo et al. (2022)
    • arXiv:2203.10465
    • This paper proposes a self-supervised graph neural network (GNN) approach to detect money laundering in Bitcoin transactions.

Illicit Financial Flows

  • The Shape of Money Laundering: Subgraph Representation Learning on the Blockchain with the Elliptic2 Dataset
    • Claudio Bellei et al. (2024)
    • arXiv:2404.19109
    • This paper uses subgraph representation learning to identify money laundering patterns in the Elliptic2 dataset.
  • Do Illicit Financial Flows Hurt Tax Revenues? Evidence from the Developing World
    • Jean-Louis Combes et al. (2021)
    • Policy Research Working Paper Series 9781, The World Bank
    • This paper examines the impact of illicit financial flows on tax revenues in developing countries.

Graph Neural Networks in Financial Applications

  • A Review on Graph Neural Network Methods in Financial Applications
    • Jianian Wang et al. (2021)
    • arXiv:2111.15367
    • This paper provides a comprehensive review of graph neural network methods applied to various financial applications, including credit risk assessment and portfolio optimization.

Note: The full author names and affiliations are not included in the original list, but can be added if necessary for citation purposes.