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 Singh, Ajay; Sharma, Ambu; Kumar, Pawan; Taneja, Sanjay; Bhatnagar, Mukul Title A name given to the resource Deep Learning for Uncovering of Fraud: A Design for Automated Financial Protection 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 Lecture Notes in Networks and Systems;Volume;1464 LNNS;pp.393-403 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-7520-3_33" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-7520-3_33</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105020238444?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020238444?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 Singh A., Christ (Deemed to be University), Karnataka, Bengaluru, India; Sharma A., Chandigarh University, Chandigarh, India; Kumar P., Graphic Era Deemed to be University, Dehradun, India; Taneja S., Graphic Era Deemed to be University, Dehradun, India; Bhatnagar M., Graphic Era Deemed to be University, Dehradun, India Description An account of the resource Leveraging the unparalleled adaptability and hierarchical feature stratification capabilities of deep learning, this study constructs a sophisticated framework for fraud detection, seamlessly integrating convolution and recurrent neural architectures with advanced anomaly detection algorithms to decode complex, nonlinear transactional patterns within heterogeneous financial datasets, thereby enabling real-time fraud identification while addressing pivotal challenges of algorithmic interpretability, adversarial resilience, regulatory compliance, scalability, and data confidentiality, ultimately redefining the paradigm of automated financial security in an increasingly digitized global economy. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Artificial intelligence; Deep learning; Financial security; Machine learning and fraud detection; Neural network; Robust Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981967519-7; 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