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 Anthraper, Joseph Abraham; Upreti, Kamal Title A name given to the resource Fraud Prevention in Banking: Innovative Techniques for Detecting Payment Fraud 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 Intelligent Control, Computing and Communications, IC3 2025;pp.144-151 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/IC363308.2025.10957594" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IC363308.2025.10957594</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105003909444?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105003909444?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 Anthraper J.A., Christ (Deemed to be University), Dept. of Computer Science, Delhi NCR, Ghaziabad, India; Upreti K., Christ (Deemed to be University), Dept. of Computer Science, Delhi NCR, Ghaziabad, India Description An account of the resource Fraud detection in banking remains one of the most critical challenges, as fraudulent patterns continue changing to avoid detection. Classic rule-based methods provide a basis approach but often lead to high rates of false positives and negatives, which limiting their efficiency. Due to the rapid growth of fraud particularly in Banking Payments, tackling this challenge has become imperative. To this end, we employ the Banksim dataset-a synthetic tool that replicates the various payment behavior of customers- to assess a number of machine learning models, Support Vector Machines, Random Forest, Logistic Regression, AdaBoost and Decision trees. Our model evaluation, using confusion matrices and classification reports, demonstrates the ability of these approaches to provide precision and reliability in detecting fraudulent transactions. This research contributes to enhancing the reliability and integrity of banking services through fraud payment detection improvements. 2025 IEEE. Subject The topic of the resource banking; Banksim dataset; Fraud detection; machine learning Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152749-5; 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