DETECTING CREDIT CARD FRAUD USING MACHINE LEARNING

Abstract

Bank fraud is "The unauthorized use of an individual's confidential
information to make purchases or withdraw funds from a user's account." E-commerce
is growing rapidly, and the world is moving towards digitization, cashless transactions,
the use of credit cards, the number of users is rapidly increasing, and with it the number
of frauds associated with it. Due to the development of technology and the increase in
the number of online transactions, fraud is also increasing, leading to huge financial
losses. Therefore, effective methods to reduce losses are needed. In addition, scammers
find ways to steal the user's credit card information by sending fake SMS and calls,
as well as by masquerade attacks, phishing attacks, and so on. This article aims to use
several machine learning algorithms such as Support Vector Machine (SVM), Decision
Tree, Bayesian Belief Networks, Logistic Regression, k-Nearest Neighbor (Knn), and
Artificial Neural Network (ANN) to predict the occurrence of fraud. In addition, we
differentiate between the implemented supervised machine learning and deep learning
methods to distinguish between fraudulent and non-fraudulent transactions.