An intelligent framework for credit card fraud detection through data analytics

Chakrabarti, Y. Ravi Kumar; Unhelkar, Bhuvan; Shankar, S. Siva; Chakrabarti, Tulika; Chakrabarti, Prasun; Sivaneasan, B.; Margala, Martin

doi:10.47974/JSMS-1320

Open Access Research Article

An intelligent framework for credit card fraud detection through data analytics

^*Y. Ravi Kumar ChakrabartiCorresponding authorravikumar.yeetha@gmail.comVemu Institute of TechnologyAutonomous InstituteChittor, Andhra Pradesh, 517112, IndiaView full profile → , Bhuvan Unhelkarbunhelkar@usf.eduMuma College of Business8350 N. Tamiami Trail SarasotaUniversity of South FloridaFlorida, FL 34243, U.S.A.0000-0003-1118-3837View full profile → , S. Siva Shankardrsivashankars@gmail.comDepartment of Computer Science and EngineeringKG Reddy College of Engineering and TechnologyMoinabad, Telangana, 500075, India0000-0002-7616-6088View full profile → , Tulika Chakrabartitulika.chakrabarti@spsu.ac.inDepartment of ChemistrySir Padampat Singhania UniversityUdaipur, Rajasthan, 313601, lndiaView full profile → , Prasun Chakrabartidrprasun.cse@gmail.comDepartment of Computer Science and EngineeringSir Padampat Singhania UniversityUdaipur, Rajasthan, 313601, lndia0000-0001-8062-4144View full profile → , B. Sivaneasansivaneasan@singaporetech.edu.sgSpecialist Adult Educator EngineeringElectrical Power Engineering Programme, 1 Punggol Coast RoadSingapore Institute of Technology828608, SingaporeView full profile → , Martin Margalamartin.margala@louisiana.eduSchool of Computing and InformaticsUniversity of Louisiana at LafayetteLA 70503, U.S.A.View full profile →

* Corresponding author · click or hover a name for details

pp. 139–149Vol. 28Issue 1January 2025DOI: 10.47974/JSMS-1320XML

Received:: 13 Feb 2024
Published Online:: 15 Jan 2025
Article type:: Research Article
Language:: EN
Article no.:: JSMS-1320
Pages:: 139–149

Abstract

An intelligent framework combining Autoencoder Neural Networks and the Dragonfly optimization algorithm, which would be put forth to this research to combat effective credit card fraud by detecting fraudulent transactions quickly through extracting important features and patterns in transactional data with the help of Autoencoder Neural Networks. The Dragonfly optimization algorithm enhances the recital of the archetypal in question by refining the hyperparameters of the autoencoder archetypal. In doing so, the algorithm improves adaptability to emerging fraud patterns and always gives strong generalizations. Critical experiments are conducted that show that the framework has enormous precision, accuracy, F1 score, specificity, as well as recall while trying to detect credit card fraud as accurately as possible, reaching a maximum accuracy of 98%. This framework, therefore, will prove to be a good defence against credit card fraud by finally protecting financial interests, based on drawing the benefits of Autoencoder Neural Networks and Dragonfly optimization.

Keywords

Credit card fraud Autoencoder Dragonfly algorithm Data analytics Data-centric methodology

Subject Classifications

68T0768T10

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