In this study, we introduce HyperGraphViz, a Hypergraph Neural Network (HGNN)-based AI framework designed for context-aware email communication analysis using the Enron Email Dataset. Traditional graph-based models fail to capture multi-recipient interactions, limiting their ability to detect fraud and insider threats. HyperGraphViz addresses this limitation by leveraging hypergraph structures to model complex, hierarchical relationships within corporate networks. The model employs Hypergraph Convolutional Networks (HGCN) for feature extraction and anomaly detection, coupled with natural language processing (NLP) techniques for analyzing email content. The experimental results demonstrate that HyperGraphViz outperforms conventional models, achieving an accuracy of 91.3% and an AUC-ROC score of 95.4%, while also providing superior fraud detection capabilities with a 93.1% anomaly detection rate and only 5.8% false positives. The system includes an interactive dashboard for real-time network analysis, making it an effective tool for fraud detection, corporate governance and risk assessment. Existing graph-based visualization methods often fail to represent multi-relational and context-dependent data structures effectively. This paper proposes HyperGraphViz, a hypergraph-based AI framework that enhances context-aware visualization. Experimental results demonstrate a 15% improvement in interpretability over baseline graph layouts.