The field of fraud detection and illicit activity analysis is moving towards the development of more sophisticated methods for identifying and preventing fraudulent behaviors in complex networks. Researchers are exploring new approaches that leverage geometric measures, graph-based frameworks, and machine learning models to uncover hidden patterns and anomalies in large datasets. These innovative methods are being applied to various domains, including public procurement, blockchain infrastructure, and cryptocurrency transactions. Notably, the use of structural asymmetry and information theory is providing new insights into the detection of collusive behaviors and fraudulent activities. Furthermore, the analysis of cross-chain transactions and liquidity pool-based protocols is highlighting the need for more effective defenses against sandwich attacks and rug pull scams. Overall, the field is advancing rapidly, with a focus on developing more effective tools and strategies for detecting and preventing illicit activities in complex networks. Noteworthy papers include: The paper introducing Heron's Information Coefficient is particularly noteworthy for its innovative approach to detecting collusion in public procurement networks. The work on cross-chain sandwich attacks is also notable for its identification of a critical vulnerability in cross-chain bridge protocols.