Financial forensics has an important role in the field of finance to detect and investigate the occurrence of finance related crimes like money laundering. However, as with other forms of criminal activities, the forensics analysis of such activities is a complex undertaking with attempts by the adversaries to constantly upgrade their ability to evade detection. Also, the extent of the volume and complexity of financial activities or transactions further complicates the task of performing financial forensics. Machine Learning or Artificial Intelligence algorithms could be used to deal with such complexities. However, the challenge of limitedly available labeled datasets especially with fraudulent activities limits the means to develop efficient algorithms. Additionally, the complexity of defining precise search patterns of evasive fraudulent transactions further complicates this challenge. In this paper, we developed a novel deep set classifier algorithm based on meta learning and applied it to deal with the complexity deriving patterns of interest with sample of limitedly labelled transactions to detect fraudulent cryptocurrency money laundering transactions. We a unique approach to train our model with progressive provision of samples and the test result exceeds leading research algorithms.

翻译：金融取证在金融领域中扮演着重要角色，用于检测和调查洗钱等金融相关犯罪的发生。然而，与其他形式的犯罪活动类似，此类活动的取证分析是一项复杂的任务，因为对手方不断试图提升其规避检测的能力。此外，金融活动或交易的数量与复杂性进一步加剧了执行金融取证的难度。机器学习或人工智能算法可用于应对此类复杂性。然而，尤其是涉及欺诈活动的标记数据集有限，这制约了开发高效算法的手段。此外，定义规避性欺诈交易的精确搜索模式的复杂性进一步加剧了这一挑战。本文基于元学习开发了一种新型深度集分类器算法，并将其应用于处理从有限标记交易样本中提取感兴趣模式的复杂性，以检测欺诈性加密货币洗钱交易。我们采用了一种独特的方法，通过逐步提供样本来训练模型，测试结果超越了领先的研究算法。