Badal-Valero Elena, Alvarez-Jareño José A, Pavía Jose M
Department of Applied Economics, University of Valencia, Avenida de los Naranjos, s/n, 46022 Valencia, Spain.
Department of Applied Economics, University of Valencia, Avenida de los Naranjos, s/n, 46022 Valencia, Spain.
Forensic Sci Int. 2018 Jan;282:24-34. doi: 10.1016/j.forsciint.2017.11.008. Epub 2017 Nov 11.
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.
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.
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.
A new tool to detect money laundering criminals is proposed in this paper. The tool is tested in the context of a real case.
本文基于对一个大型洗钱案例操作数据库的分析，该案例涉及一家核心公司及其一批供应商，其中26家已被警方认定为欺诈公司。鉴于有充分理由怀疑更多公司实施了犯罪行为，且为了更有效地利用极为有限的警方资源，我们旨在构建一种工具来检测洗钱罪犯。
我们将本福特定律与机器学习算法（逻辑回归、决策树、神经网络和随机森林）相结合，在西班牙一个真实法庭案件的背景下寻找洗钱罪犯的模式。
使用本福特定律将每个供应商的会计数据集映射到一个21维空间，并应用机器学习算法后，标记出了可能值得进一步审查的其他公司。
本文提出了一种检测洗钱罪犯的新工具。该工具在一个真实案例的背景下进行了测试。
