DETECTION OF CREDIT CARD FRAUD USING MACHINE LEARNING MODELS

Abstract

Nowadays, credit card fraud has increased because of all the traditional payment modes being converted into online payment modes. Individuals and financial organizations face heavy losses every year due to fraud. The customer is very scared because of the scam. Frauds and heavy losses destroy the business of financial institutions; hence, researchers give an overview of who detects fraudulent activities. The second researcher used machine learning algorithms for scam detection. This research focuses on machine-learning algorithms that detect scams and scammers. It reveals that a machine learning approach can predict whether the transaction is a scam. In this research, the machine learning algorithms were applied using the European dataset gathered from Kaggle. The imbalance is because there are many genuine and very few fraudulent transactions. These algorithms are also used to identify genuine and fraudulent transactions.  All models have the same accuracy of 99%. However, compared to the other models, a random forest has better recall, precision, and an F1 score in its evaluation metrics. Therefore, a random forest comparatively shows better results than logistic regression and a decision tree.