Fraud Detection in Credit Card Transactions using Anomaly Detection

Abstract

Credit Card is a convenient payment mode. It is useful for both online and offline modes of payment. For online, we need to use the Credit Card Number. The Credit Card Number is sufficient for online transactions and that comes with a risk. We have fraud transaction detection systems but they can detect it only after the occurrence of transactions. The Organizations keep the detailed data consisting of genuine transactions as well as  fraudulent transactions. The fraudulent are generally caught following a particular pattern. It is a difficult task to analyze each and every transaction data among about millions and billions of them. Predictive Algorithms could be a valuable asset for the detection of fraudulent transactions, here we need Data Mining. A variety of statistical tests could be used for the prevention of fraud events .However, we still have no perfect method for detecting fraudulent transactions. To, the banks, these frauds are a major financial issues. The detection of fraudulent transactions among the genuine transactions is totally skewed towards the latter. According the estimation, out of 12 billion transactions made in a year, 10 million are frauds. We are using isolation forest algorithm and local outlier factor algorithm to analyze and predict the frauds. The accuracy and errors of both the data has also been computed.