Transformer-Driven Financial Process Automation Optimization

Abstract

The increasing complexity of enterprise financial d ata p rocessing p oses significant challenges for intelligent information systems that require efficient s emantic u nderstanding a nd real-time decision support. These requirements are also relevant to communication-enabled infrastructures and electromagnetic information environments, where heterogeneous data integration and rapid risk perception are essential for reliable system operation. To address these issues, this study proposes a Transformer-driven financial process automation framework that integrates semantic modeling, multimodal data fusion, reinforcement learning-based process optimization, anomaly detection, and knowledge graph construction. A pretrained Transformer is employed to capture semantic dependencies from financial documents, while structured and unstructured information is jointly modeled through cross-modal attention mechanisms. The reinforcement learning policy network dynamically adjusts process execution paths to improve operational efficiency, a nd a ttention d istribution a nalysis c ombined w ith c lustering t echniques enables accurate anomaly identification and risk warning generation. In addition, a semantic knowledge graph is established to support interpretable process management and intelligent decision-making. Experimental results demonstrate that the proposed framework achieves 95.72% anomaly detection recall, 93.57% warning precision, and substantially reduces execution latency compared with conventional automation systems while improving semantic recognition accuracy across multiple financial entities. The proposed approach provides an effective solution for intelligent financial process optimization and offers valuable methodological insights for data-driven monitoring, semantic information processing, and adaptive decision support in communication-intensive and electromagnetic sensing applications.