A Boosted Deep ConVNet embedding Long Short Term Memory with Synthetic Minority Oversampling Techniques as Foiling Model for Payment Card Fraud - JIFT

Research Articie 13 June 2025

A Boosted Deep ConVNet embedding Long Short Term Memory with Synthetic Minority Oversampling Techniques as Foiling Model for Payment Card Fraud

Lateef Gbolahan Salaudeen ¹

lsalaudeen@chrislanduniversity.edu.ng

, Danlami Gabia ² , Muhammad Garbaa ³ , Hassan Umar Surua ⁴ show author's information

1 : Kebbi State University of Science and Technology, Aliero, P.M.B 1144, Aliero, Kebbi State, Nigeria；Department of Computer Science, College of Natural and Applied Sciences Chrisland University, Abeokuta, PMB 2135 Owode-Ajebo Road, Abeokuta, Nigeria
2 : Kebbi State University of Science and Technology, Aliero, P.M.B 1144, Aliero, Kebbi State, Nigeria
3 : Kebbi State University of Science and Technology, Aliero, P.M.B 1144, Aliero, Kebbi State, Nigeria
4 : Kebbi State University of Science and Technology, Aliero, P.M.B 1144, Aliero, Kebbi State, Nigeria

DOI: 10.55578/jift.2506.005

Keywords: Fraud Detection System , Imbalance Dataset , Deep Learning , Financial Institutions