AI Article Synopsis
- The study analyzes a money laundering case involving a core company and its fraudulent suppliers to develop a detection tool.
- By combining Benford's Law with various machine learning algorithms, the researchers aim to identify patterns related to money laundering activities.
- The tool successfully flags additional companies for further investigation, demonstrating its effectiveness in a real-world legal context.
Article Abstract
Objectives: This paper is based on the analysis of the database of operations from a macro-case on money laundering orchestrated between a core company and a group of its suppliers, 26 of which had already been identified by the police as fraudulent companies. In the face of a well-founded suspicion that more companies have perpetrated criminal acts and in order to make better use of what are very limited police resources, we aim to construct a tool to detect money laundering criminals.
Methods: We combine Benford's Law and machine learning algorithms (logistic regression, decision trees, neural networks, and random forests) to find patterns of money laundering criminals in the context of a real Spanish court case.
Results: After mapping each supplier's set of accounting data into a 21-dimensional space using Benford's Law and applying machine learning algorithms, additional companies that could merit further scrutiny are flagged up.
Conclusions: A new tool to detect money laundering criminals is proposed in this paper. The tool is tested in the context of a real case.
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| http://dx.doi.org/10.1016/j.forsciint.2017.11.008 | DOI Listing |
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