Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sistla, Swetha; Sankaran, Mohan; Jooluri, Nagaraju; Shruthaalaxmi; Devesh, Sonal; Vaz, Sonia Title A name given to the resource Federated and Explainable AI Models for Secure FinTech Transactions in Digital Manufacturing Supply Chains Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived 2026 Innovations in Machine, Engineering, and Digital Conference, IMED 2026; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/IMED68921.2026.11484363" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IMED68921.2026.11484363</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105038336654?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038336654?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sistla S., FinTech &amp; Ai Solutions Infosys Limited, Richmond, 23060, VA, United States; Sankaran M., Software Engineering PayPal Inc., 94555, CA, United States; Jooluri N., Incode Technologies, Software Engineering, 20148, VA, United States; Shruthaalaxmi, University of Toronto, Department of Public Policy And Statistics, Toronto, Canada; Devesh S., Christ University (Deemed To Be University), School of Business And Management, Karnataka, Bangalore, 560073, India; Vaz S., Rosary College of Commerce And Arts, Department of Economics, Goa, Navelim, 403707, India Description An account of the resource Digital 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. 2026 IEEE. Subject The topic of the resource Explainable Artificial Intelligence; Federated Learning; FinTech; Manufacturing; Supply Chain Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833156997-6; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online