The rise of digital payments has caused consequential changes in the financial crime landscape. As a result, traditional fraud detection approaches such as rule-based systems have largely become ineffective. AI and machine learning solutions using graph computing principles have gained significant interest in recent years. Graph-based techniques provide unique solution opportunities for financial crime detection. However, implementing such solutions at industrial-scale in real-time financial transaction processing systems has brought numerous application challenges to light. In this paper, we discuss the implementation difficulties current and next-generation graph solutions face. Furthermore, financial crime and digital payments trends indicate emerging challenges in the continued effectiveness of the detection techniques. We analyze the threat landscape and argue that it provides key insights for developing graph-based solutions.

翻译：数字付款的上升导致了金融犯罪格局的随之变化,因此,传统欺诈侦查方法,如基于规则的系统,基本上已经失效。近年来,使用图表计算原则的人工智能和机器学习方法引起了极大的兴趣。基于图表的技术为侦查金融犯罪提供了独特的解决办法机会。然而,在工业规模的实时金融交易处理系统中实施这类解决办法,暴露了许多应用挑战。我们在本文件中讨论了当前和下一代图表解决方案面临的实施困难。此外,金融犯罪和数字付款趋势表明侦查技术持续有效性方面新出现的挑战。我们分析了威胁情景,并论证它为开发基于图表的解决方案提供了关键的洞察力。