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 Book Chapter Faculty Publications- Book Chapter 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 Yadav, Sameer; Singh, Archana; Peddapalli, Nagaraj; Chauhan, Amit; Ahmed, Syed Arfath; Bhoopathy, V. Title A name given to the resource ML Use in Fraud Detection in the Financial Sector 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 Utilizing AI and Machine Learning in Financial Analysis;pp.427-447 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3693-8507-4.ch0023" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-8507-4.ch0023</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105007268498?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105007268498?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 Yadav S., University of Allahabad, India; Singh A., University of Allahabad, India; Peddapalli N., GITAM School of Business, GITAM University, India; Chauhan A., Christ University, India; Ahmed S.A., Maulana Azad National Urdu University, India; Bhoopathy V., Sree Rama Engineering College, India Description An account of the resource Detecting fraud is complex, requiring analysis of vast data over time, making it resource- intensive for human investigators. This chapter explores key forms of financial fraud, including those involving corporations, banks, and insurance sectors. It provides a thorough evaluation of various machine learning techniques, such as supervised and unsupervised learning, deep learning, and ensemble methods, used to detect and prevent these financial crimes. The chapter examines the effectiveness of algorithms like LR, DT, SVM, and NN in identifying complex fraud patterns. Additionally, it discusses advanced approaches like anomaly detection, clustering, and real- time fraud detection systems. It highlights ongoing challenges and proposes potential future developments, including explainable AI, federated learning, and improved data processing techniques. This comprehensive exploration aims to deepen the understanding of machine learning's role in fraud detection and guide future research toward more effective and reliable fraud prevention strategies. 2025 by IGI Global Scientific Publishing. All rights reserved. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-836938509-8; 979-836938510-4; Language A language of the resource English Type The nature or genre of the resource Book chapter 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