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.

中文翻译：

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金融犯罪和欺诈发现的图形计算：趋势，挑战和展望

数字支付的兴起已导致金融犯罪领域发生了相应的变化。结果，诸如基于规则的系统之类的传统欺诈检测方法在很大程度上已经失效。近年来，使用图计算原理的AI和机器学习解决方案引起了人们的极大兴趣。基于图的技术为金融犯罪检测提供了独特的解决方案机会。但是，在工业规模的实时金融交易处理系统中实施此类解决方案带来了许多应用挑战。在本文中，我们讨论了当前和下一代图形解决方案所面临的实现困难。此外，金融犯罪和数字支付趋势表明检测技术的持续有效性方面出现了新的挑战。