Advancing Enterprise Financial Control and Risk Management through Mobile Interactive Technologies

Abstract

With the deep integration of the digital economy and enterprise operations, mobile interactive technologies—characterized by real-time connectivity and ubiquitous access—have emerged as a critical solution to overcoming the spatial and temporal limitations inherent in traditional financial monitoring. However, existing research predominantly relies on conventional information technology frameworks, which exhibit substantial limitations in multidimensional financial data correlation mining, lightweight deployment on mobile terminals, and the integration and analysis of heterogeneous data. These limitations hinder the timely identification of risks in dynamic business environments. In this study, a financial monitoring algorithmic framework was proposed for enterprise environments under mobile interactive networks, integrating self-attention mechanisms and graph convolutional networks (GCNs). A formal definition of financial data monitoring was established, and a hierarchical algorithmic architecture was developed. Attribute-wise and relation-wise self-attention mechanisms were employed to capture the multidimensional characteristics of financial entities and the dynamic associations among transaction participants. GCNs were utilized to extract deep semantic representations from heterogeneous graph data. In addition, a domain-specific loss function and anomaly detection mechanism were designed based on financial business rules. The proposed framework addresses the limitations of traditional monitoring methods reliant on structured data and offers a robust, real-time solution for dynamic financial surveillance across regions and systems. The findings contribute both theoretical and practical value by advancing the precision of enterprise financial control and supporting the development of intelligent risk management systems.