With increased internet usage, online transactions have been on the rise. One of the most prevalent problems faced is credit cards frauds. While web applications and mailing services are heavily spammed, the upsurge of handheld mobile devices has led to an outburst of heavy mobile credit card spamming. The matter is more severe in mobile devices due to lesser sophisticated filtering mechanisms in built in mobile operating systems. Recent advancements in electronic commerce and communication systems have significantly increased the use of credit cards for both online and regular transactions. However, there has been a steady rise in fraudulent credit card transactions, costing financial companies huge losses every year. The development of effective fraud detection algorithms is vital in minimizing these losses, but it is challenging because most credit card datasets are highly imbalanced. Traditional rule-based systems are often insufficient to handle the sophisticated and evolving techniques fraudsters use. Machine learning (ML) provides more dynamic, scalable, and effective methods for detecting fraudulent activities. This paper presents a comprehensive review on credit card datasets, imbalanced nature of datasets and existing baseline techniques in the domain.

Keywords— Credit Card Fraud Detection, Machine Learning, Feature Selection,  Imbalanced  Datasets, Classification Accuracy