Evaluation of Machine Learning Algorithms to Detect Credit Card Fraud
Credit card fraud detection is very serious issue nowadays. Every person from youth to aged requires a credit card. Credit card fraud is generally done during online transactions. Information regarding pin is obtained illegally. It is termed as shoulder surﬁng. Some other ways are card stealing, Buying audit cards, Information and web Traffic, etc. After obtaining information illegally online transactions are made. In many companies fraud credit cards are identified so that customers are not charged for unnecessary equipment. There is a vast need of credit card fraud detection. If proper amount of data is collected, credit card fraud can be detected using machine learning algorithms. In this paper, supervised and unsupervised machine learning algorithms have been applied to detect credit card frauds in a highly imbalanced dataset. It was found that unsupervised machine learning algorithms can handle the skewness and give best classiﬁcations result.