Vaz, Sonia2026-05-052026-05-052026Sistla, S., Sankaran, M., Jooluri, N., Devesh, S., & Vaz, S. (2026, March). Federated and Explainable AI Models for Secure FinTech Transactions in Digital Manufacturing Supply Chains. In 2026 Innovations in Machine, Engineering, and Digital Conference (IMED) (pp. 1-6). IEEE.http://rcca.ndl.gov.in/handle/123456789/557Digital manufacturing supply chains are becoming increasingly dependent on inbuilt FinTech services to perform automated payments, invoicing, and settlements which presents sensitive financial and operational data to security and privacy threats. This article is an empirical paper concerning the application of Federated Learning (FL) and Explainable Artificial Intelligence (XAI) in securing FinTech transactions in decentralized manufacturing supply chains. The suggested framework will facilitate joint fraud and anomaly-related detection without exchanging raw data between supply-chain participants. Different privacy mechanisms such as client-level and secure aggregation are integrated to safeguard sensitive data and minimize the risks of inferences. Explainable AI methods are used such as SHAP, local surrogate models, to enable transparency and auditability as well as regulatory compliance. Experimental evidence has shown that federated models can attain almost centralized detection accuracy with much stronger privacy guarantees and explainability procedures can give insightful and interpretable information about model decisions. The paper identifies the trade-offs between accuracy, privacy, and computational overhead and concludes that federated and explainable AI provides a convenient, secure, and compliant solution to FinTech-enabled digital manufacturing ecosystems.enArticle