Authors:
Sonjoy Ranjon Das, Antigoni Kruti, Rajan Devkota, Rejwan Bin Sulaiman

Addresses:
1,2,4Department of Computer Science and Technology, Northumbria University, England, United Kingdom. 3Department of Computer Engineering, Tribhuvan University, Kirtipur, Nepal. sonjoy.das@northumbria.ac.uk1, antigoni.kruti@northumbria.ac.uk2, pas075bct033@wrc.edu.npuk3, rejwan.sulaiman@northumbria.ac.uk4

Abstract:

Credit card fraud has increased vulnerability effects due to the large usage functions for customers due to innovative technologies and communication patterns. This article presents a review and important analysis of credit card fraud detection and prediction of fraudulent transactions based on cutting-edge research. The study provides a limited investigation into deep machine learning to address the effects of data issues on credit card fraud detection through the design of robust solutions. This study aims to develop a mechanism with classifiers, such as Artificial Neural Network (ANN), Support Vector Machine (SVM), and Naive Byes, that contain vectors of information sequence properties, structure and mechanisms. Simultaneously, diverse experiments are developed to analyze the proposed approach to datasets. The framework enhances a comprehensive financial security diverse approach to suspicious financial activities on stakeholders' assets. It emphasizes the significance of mitigation and detection capabilities for potential threats to safeguard financial transactions. The outcome of this research demonstrates a robust solution for real scenarios of credit card fraud detection, considering model abilities with high accuracy rates that address the limitations of integrated factors.

Keywords: Credit Card Fraud Detection; Machine Learning; Artificial Neutral Network; Support Vector Machine; Naïve Byes; Class Imbalance; Exploratory Data Analysis; Logistic Regression and Random Forest.

Received on: 19/10/2022, Revised on: 21/01/2023, Accepted on: 05/04/2023, Published on: 07/12/2023

FMDB Transactions on Sustainable Technoprise Letters, 2023 Vol. 1 No. 2, Pages: 70-81

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