Credit Card Fraud Detector Based on Machine Learning Techniques

Abstract

The massive development of technology has affected commerce and given rise to e-commerce and online shopping. Nowadays, consumers prioritize e-shopping over the brick and motor stores due to numerous benefits, including time and transport convenience. However, this progressive upsurge in online payment increases the number of credit card frauds. Therefore, defending against fraudsters’ activity is obligatory and can be achieved by securing credit card transactions. The objective of this paper is to build a model for credit card fraud detection using Machine learning techniques. An innovative approach to credit card fraud detection grounded on machine learning is proposed in this study. Machine learning (ML) is an artificial intelligence subfield comprising learning techniques from experience and completing tasks without being explicitly programmed. Three ML techniques have been used: Support vector machine, logistic regression, Random Forest, and Artificial Neural network. First, the most significant features that affect the type of transaction (fraud or not fraud) have been selected. After that, the ML model was applied. The performance of the proposed approach is tested using a confusion matrix, recall, precision, f-measure, and accuracy. The proposed method is tested using accurate data that consists of 284807 transactions. The result shows the efficiency of the proposed approach.