The recent advancements in the field of machine learning applications for mental health and financial fraud detection are notably shifting towards more sophisticated graph-based neural network architectures. These developments aim to address the inherent complexities and dynamic nature of the data in these domains. In mental health, there is a growing emphasis on integrating heterogeneous data sources, such as functional and structural brain imaging, to enhance diagnostic accuracy for conditions like mild cognitive impairment. This trend is exemplified by the use of heterogeneous graph neural networks (HGNNs) that effectively capture the rich heterogeneity of dual-modal information, leading to superior classification performance. Similarly, in financial fraud detection, the field is witnessing a move away from traditional rule-based and simple machine learning methods towards more adaptive and context-aware models. The introduction of graph neural networks (GNNs) with attention mechanisms and efficient neighborhood sampling methods is proving to be particularly effective in detecting complex fraud patterns that evolve over time. Additionally, the incorporation of human-in-the-loop feedback mechanisms is further enhancing the robustness and adaptability of these models, particularly in scenarios where fraud patterns are sparse and rapidly changing. Overall, these innovations signify a significant leap forward in the ability of machine learning models to handle the nuanced and multifaceted data challenges present in both mental health diagnosis and financial fraud detection.