AI-Powered FinTech Analytics for Transactional Transparency and Fraud Mitigation in Industrial IoT Ecosystems

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dc.contributor.authorVaz, Sonia
dc.date.accessioned2026-05-05T10:54:08Z
dc.date.available2026-05-05T10:54:08Z
dc.date.issued2026
dc.description.abstractThe 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.
dc.identifier.citationSistla, 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.
dc.identifier.urihttp://rcca.ndl.gov.in/handle/123456789/559
dc.language.isoen
dc.publisherInnovations in Machine, Engineering, and Digital Conference (IMED) (pp. 1-7)
dc.titleAI-Powered FinTech Analytics for Transactional Transparency and Fraud Mitigation in Industrial IoT Ecosystems
dc.typeArticle
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