Forensic Science International 282, pp. 24-34.
ISSN/ISBN: Not available at this time. DOI: 10.1016/j.forsciint.2017.11.008
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.
Bibtex:
@article{,
title = "Combining {Benford's Law} and machine learning to detect money laundering. An actual Spanish court case",
journal = "Forensic Science International",
volume = "282",
pages = "24--34",
year = "2018",
issn = "0379-0738",
doi = "https://doi.org/10.1016/j.forsciint.2017.11.008",
url = "http://www.sciencedirect.com/science/article/pii/S0379073817304644",
author = "Elena Badal-Valero and Jos{\'e} A. Alvarez-Jare{\~n}o and Jose M. Pav{\'i}a",
}
Reference Type: Journal Article
Subject Area(s): Accounting