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 Mehndiratta, Vandana; Gupta, Varuna; Srivastava, Shilpa; Singh, Meenu Title A name given to the resource Artificial Intelligence and Machine Learning in Financial Fraud Research: A Bibliometric Analysis of Trends and Collaborations Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 International Conference on Emerging Technologies and Innovation for Sustainability, EmergIN 2025;pp.1100-1104 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/EmergIN67762.2025.11450892" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/EmergIN67762.2025.11450892</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036390358?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036390358?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 Mehndiratta V., Christ University, School of Sciences, Bengaluru, India; Gupta V., Christ University, School of Sciences, Bengaluru, India; Srivastava S., Christ University, School of Sciences, Bengaluru, India; Singh M., VB-Technical University of Ostrava, Faculty of Electrical Engineering and Computer Science, Ostrava, Czech Republic Description An account of the resource This study conducts a bibliometric analysis of the financial fraud research domain, synthesizing publication metadata to uncover collaboration structures, thematic hotspots, and temporal trends. The research utilizes the bibliometric tool Vos viewer to retrieve the insights on the various aspects of financial fraud detection using the emerging technologies. The study analyses the integration of AI and machine learning techniques on the detection of financial fraud. Using co-authorship networks, we identify influential authors and collaboration clusters, while keyword frequency and co-occurrence patterns reveal core topics and emerging fronts such as anomaly detection, machine learning, insider trading, and regulatory analytics. Temporal profiling of publications highlights growth phases and shifts in emphasis across years and venues. The analysis provides an integrated view of the field's intellectual structure, enabling scholars and practitioners to locate key contributors, benchmark thematic coverage, and identify gaps for future inquiry. Results offer actionable insights for forming research collaborations, prioritizing topics, and designing evidence- based strategies against financial fraud. 2025 IEEE. Subject The topic of the resource bibliometrics; co-authorship; co-occurrence; financial fraud; scient metrics; topic modelling Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155603-7; 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