Credit card Fraud Detection Using Machine Learning

Abstract

In today’s world, the most easiest mode of payment is credit card for both online and offline. It helps in providing cashless shopping across the globe. Fraud event occurs only during online payment as credit card number is sufficient to make transaction which will be on the credit card to make online payment but for offline payment password will be asked so during offline transaction frauds cannot occur. In the existing system of detecting fraud transaction, the fraud is detected after the transaction is done. Companies have a detailed analysis of transactional and fraud data. Frauds tends to appear in patterns. In billions of credit card transactions, it is quite difficult to analyse each in isolation. Having predictive algorithms can help to detect fraudulent transactions. this is how data mining comes into play. Data consists of combination of continuous data and nominal data. We can use variety statistical tests to prevent fraud events. Detecting credit card fraud is still not a perfect science. While fraud is still a major financial issue to banks, the distribution of fraud to non-fraudulent transactions is severely skewed towards non-fraudulent transactions. To analyse and predict fraud events we have used local outlier factor and isolation forest algorithms and thus calculated number of fraud transactions. We have calculated the accuracy and number of errors of both the algorithms.