Credit Card Froud Detection Using Machine Learning

Abstract

This project aims to find the most efficient machine learning models to detect fraudulent transactions on credit cards. The dataset used for this project consists of credit card transactions made by European cardholders in September 2013. This dataset presents transactions that have occurred in two days, where there are 492 frauds out of 284,807 transactions. Machine learning methods, such as decision trees, logistic regression and random forest classifier are used to predict the fraudulent transactions. Performance of these machine learning models are compared to achieve the highest accuracy. According to the results, it is found that the random forest classifier is the most effective model, and the SMOTE technique used to overcome the data imbalance performs better than the under-sampling technique. It is also observed that the models employed with the under-sampled data misclassify large number of non-fraud transactions as fraud. Lastly, by means of the random forest with the over-sampling technique (SMOTE), it is observed that the feature “V13” has the most important role in detecting fraud.