  In recent years, the unprecedented growth in digital payments fueled
consequential changes in fraud and financial crimes. In this new landscape,
traditional fraud detection approaches such as rule-based engines have largely
become ineffective. AI and machine learning solutions using graph computing
principles have gained significant interest. Graph neural networks and emerging
adaptive solutions provide compelling opportunities for the future of fraud and
financial crime detection. However, implementing the graph-based solutions in
financial transaction processing systems has brought numerous obstacles and
application considerations to light. In this paper, we overview the latest
trends in the financial crimes landscape and discuss the implementation
difficulties current and emerging graph solutions face. We argue that the
application demands and implementation challenges provide key insights in
developing effective solutions.
