Analysis of Machine and Deep Learning Approaches for Credit Card Fraud Detection

In modern days, digitalization increased more demand because of faultless, ease, and convenient use of payment online. More people are choosing to pay the money through online mode through a safe gateway in e-commerce or e-trade. Today’s reality seems we are on the fast-growing to a cashless society. As indicated by the World Bank Report in the year of 2018 most of transactions are non-cash and also increased to 25%. Because of so many banking and financial companies spending more money to develop a application based on current demand. False transactions can happen in different manners and can be placed into various classifications. Learning approaches to classification play an essential role in detecting credit card fraud detection through online mode. There will be two significant reasons for the challenges of credit card detection. In the first challenge as the usage of the card has normal behavior or any fraudulent and second as most of the datasets are misrepresented for challenging to classify. In this paper, we investigate the machine and deep learning approaches usage of credit card fraud detection and other related papers and that merits and demerits and, of course, discussed challenges and opportunities.