Abstract
The rapid growth in e-commerce has resulted in an increasing number of people shopping online. These shoppers depend on credit cards as a payment method or use mobile wallets to pay for their purchases. Thus, credit cards have become the main payment method in the e-world. Given the billions of transactions that occur daily, criminals see tremendous opportunities to be gained from finding different ways of attacking and stealing credit card information. Fraudulent credit card transactions are a serious business issue, and such 'scams' can result in significant financial and personal losses. As a result, businesses are increasingly investing in the development of new ideas and methods for detecting and preventing fraud to secure their customers' trust to protect their privacy. In recent years, learning algorithms have emerged as important in research areas aimed at developing optimal solutions to this issue. The core challenge currently facing researchers is that of the imbalanced credit card dataset, in which the data are highly skewed and the number of normal transactions is much higher than fraudulent transactions, which thus negatively affects the performance of credit card fraud detection. This paper reviews the sampling techniques and their importance in solving the imbalanced data problem. Past research is found to show that hybrid sampling techniques will produce excellent results that can improve the fraud detection system.