Vaz, Sonia2026-05-052026-05-052026Sistla, S., Sankaran, M., Jooluri, N., Veernapu, K., Keer, P. K., & Vaz, S. (2026, March). AI-Powered FinTech Analytics for Transactional Transparency and Fraud Mitigation in Industrial IoT Ecosystems. In 2026 Innovations in Machine, Engineering, and Digital Conference (IMED) (pp. 1-7). IEEE.http://rcca.ndl.gov.in/handle/123456789/559The intensive introduction of Industrial Internet of Things (IIoT) technologies has already increased the quantity and complexity of machine-to-machine financial transactions, which has become a significant challenge in terms of the clarity of the story and exclusion of threats of fraud. The idea proposed in this paper is AI-based FinTech analytics that feature both supervised and non-supervised machine learning systems when detecting fraud in industrial transactional environments. Advanced feature engineering and imbalance-sensitive model evaluation metrics are used to improve detection accuracy and reliability. Explainable artificial intelligence methods have been used to foster regulatory trust. The experimental findings show that there is a high rate of fraud detection, a low rate of false alarms, and applicability in real time, which validates the efficiency of the proposed approach in light of safe and transparent IIoT-based financial systems. The superior performance of XGBoost (97.6%) is attributed to the optimization of the gradient for fraud detection.enArticle