Credit Card Fraud Detection from Imbalanced Dataset Using Machine Learning Algorithm
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© 2020 by IJCTT Journal
Volume-68 Issue-3
Year of Publication : 2020
Authors : Swati Warghade, Shubhada Desai, Vijay Patil
DOI :  10.14445/22312803/IJCTT-V68I3P105

How to Cite?

Swati Warghade, Shubhada Desai, Vijay Patil, "Credit Card Fraud Detection from Imbalanced Dataset Using Machine Learning Algorithm," International Journal of Computer Trends and Technology, vol. 68, no. 3, pp. 22-28, 2020. Crossref, 10.14445/22312803/IJCTT-V68I3P105

Abstract
In Today’s world, credit card is the most accepted payment mode for both online as well as offline, it provide cashless shopping at every shopping mall. It is the most convenient way to do online transaction. Therefore, risk of fraud credit card transaction has also been increasing. With the growing usage of credit card transactions, financial fraud crimes have also been drastically increased leading to the loss of huge amounts in the finance industry. Having an efficient fraud detection algorithm has become a necessity for all banks in order to minimize such losses. In fact, credit card fraud detection system involves a major challenge: the credit card fraud data sets are highly imbalanced since the number of fraudulent transactions is much smaller than the legitimate ones. This paper aims at analysing various machine learning techniques using various metrics for judging various classifiers. This model aims at improving fraud detection rather than misclassifying a genuine transaction as fraud.

Keywords
Credit Card Fraud Detection, Imbalanced dataset, SMOTE.

Reference
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