Despite the wide reach of anti-money laundering legislation worldwide and increasing media attention, fostered by journalistic leaks such as Panama Papers, empirical knowledge on how criminals launder their illicit proceeds is still scarce. The few available empirical studies show that money laundering (ML) schemes are often less sophisticated than they are depicted in the political and media debate. To contribute to the empirical knowledge of ML behaviour, and test this hypothesis, the present study analyses the ML activities related to 2818 Italian offenders included in the ML section of the LexisNexis’ WorldCompliance database. Through a quantitative content analysis of textual information related to each offender's profile, it highlights the characteristics of the ML offenders, the methods (or ‘typologies’ in FATF terms) employed, the assets seized, the business sectors involved and the countries in which ML was conducted. The results confirm that criminals tend to employ unsophisticated typologies, as well as prefer Italy or jurisdictions that are close (geographically and culturally) to Italy for laundering their illicit proceeds. Tangible assets (first real estate and registered vehicles) are more frequent than financial assets. Finally, differences exist between the laundering by mafia-related ML offenders and non-mafia ones. The study provides empirical ground to progress in the knowledge of how ML offenders behave, and supports the idea that criminals, when laundering their proceeds, do not act as legitimate entrepreneurs, but may be driven by other constraints and drivers.

中文翻译：

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清洗黑手党现金：对2800名意大利犯罪分子洗钱行为的实证分析

尽管反洗钱立法在全球范围内影响广泛，并且在巴拿马文件等新闻泄密事件的推动下，媒体关注度不断提高，但关于犯罪分子如何洗钱非法收益的经验知识仍然很少。少数可用的实证研究表明，洗钱 (ML) 计划通常没有政治和媒体辩论中描述的那么复杂。为了增进对洗钱行为的实证知识并检验这一假设，本研究分析了与 LexisNexis WorldCompliance 数据库的洗钱部分中包含的 2818 名意大利罪犯相关的洗钱活动。通过对与每个犯罪者概况相关的文本信息进行定量内容分析，它突出了洗钱犯罪者的特征、所采用的方法（或 FATF 术语中的“类型”）、扣押的资产、涉及的业务部门以及洗钱所在的国家/地区进行了。结果证实，犯罪分子倾向于采用简单的类型，并且更喜欢意大利或与意大利（地理和文化上）接近的司法管辖区来洗钱其非法所得。有形资产（首先是房地产和注册车辆）比金融资产更常见。最后，与黑手党相关的洗钱犯罪者和非黑手党犯罪者的洗钱行为之间存在差异。该研究为进一步了解机器学习犯罪者的行为方式提供了实证基础，并支持这样的观点：犯罪分子在洗钱时，并不是以合法企业家的身份行事，而是可能受到其他约束和驱动因素的驱动。