  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.
