Multi-Modal GAN for Combining Machine and Financial Streams

Abstract

Financial anomalies like extra billing, underreporting, and mismatching transactions often need intelligent mysterious data fusion strategies. Conventional detection models work under an assumption of using single-modal input and fail to consider complex interdependencies between operational logs and financial records. The present work proposes a first-of-its-kind multi-modal framework based on GANs for aligning machine activity logs and financial transaction streams relevant to anomaly detection. In learning the latent correlation between system performance metrics and billing events, the proposed Multi-Modal GAN can thereby pointing out inconsistencies which indicate possible wrongful behavior or operational error. Training and validation were performed on both synthetic and real datasets obtained from IoT-enabled industrial domains. The results show a significant improvement in detection accuracy and reduction in false positives when compared to baseline models, such as Autoencoders and One-Class SVMs. This research provides a prospective scalable solution for proactive financial monitoring in automation-driven industries, indicating the possibility of its real-time deployment in edge computing infrastructures and ERP-integrated audit systems.