Combining Benford's Law and machine learning to detect money laundering. An actual Spanish court case

Forensic Sci Int. 2018 Jan;282:24-34. doi: 10.1016/j.forsciint.2017.11.008. Epub 2017 Nov 11.

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.

Keywords: Crime data; Fabricated data; Fraud; Neural networks; Random forests.