With the widespread digitization of finance and the increasing popularity of cryptocurrencies, the sophistication of fraud schemes devised by cybercriminals is growing. Money laundering -- the movement of illicit funds to conceal their origins -- can cross bank and national boundaries, producing complex transaction patterns. The UN estimates 2-5\% of global GDP or \$0.8 - \$2.0 trillion dollars are laundered globally each year. Unfortunately, real data to train machine learning models to detect laundering is generally not available, and previous synthetic data generators have had significant shortcomings. A realistic, standardized, publicly-available benchmark is needed for comparing models and for the advancement of the area. To this end, this paper contributes a synthetic financial transaction dataset generator and a set of synthetically generated AML (Anti-Money Laundering) datasets. We have calibrated this agent-based generator to match real transactions as closely as possible and made the datasets public. We describe the generator in detail and demonstrate how the datasets generated can help compare different machine learning models in terms of their AML abilities. In a key way, using synthetic data in these comparisons can be even better than using real data: the ground truth labels are complete, whilst many laundering transactions in real data are never detected.

翻译：随着金融数字化全面普及以及加密货币日益流行，网络犯罪分子设计的欺诈手段日益复杂。洗钱——将非法资金转移以掩盖其来源——可跨越银行与国界，产生复杂的交易模式。据联合国估计，全球每年洗钱金额占全球GDP的2%-5%，约8000亿至2万亿美元。遗憾的是，训练机器学习模型以检测洗钱的真实数据通常不可获取，而以往的合成数据生成器存在显著缺陷。为推进该领域发展并便于模型比较，亟需一个真实、标准化且公开可用的基准数据集。为此，本文贡献了一个合成金融交易数据集生成器及一组合成生成的反洗钱（AML）数据集。我们校准了基于智能体的生成器，使其尽可能贴近真实交易，并将数据集公开。我们详细描述了该生成器，并展示了生成的数据集如何帮助比较不同机器学习模型在反洗钱能力上的差异。一个关键优势是：在此类比较中使用合成数据甚至可能优于真实数据——因为合成数据的真实标签是完整的，而真实数据中的大量洗钱交易从未被检测到。