Credit Card Fraud Detection Using MachineLearning Algorithms

Abstract

Credit Card payment facilitating people to pay for goods and service quickly. The credit payment is gaining popularity day by day because of its benefits. With the popularity of credit card payment, crime related to credit card fraud is also increasing. Credit card fraud leads to a colossal amount of loss of financial institutions like banks and the customer. Detecting fraud is costly and time-confusing, Though it is too important to detect fraud and prevent fraud in the future. In this thesis, the challenges of credit card fraud are discussed. Credit Card fraud is treated as the classification problem, and experiments are carried out with Decision Tree, Random Forest and SVM. Credit Card data will always be highly imbalanced in nature, having fewer number of frauds than normal transactions. To deal with this problem, resampling techniques are performed on the dataset. The credit card fraud problem is also considered an anomaly detection problem having fraud as an anomaly. The main objective of the research is to find an effective approach to detect fraud. This thesis compares the classification approach with the anomaly detection approach. Also, classification results are tried to improve using data level resampling techniques. Comparison results are discussed in Result Section.