Credit Card Fraud Detection Using Machine Learning

Abstract

Unprecedented advancement of e-commerce soars the frequency of online and offline financial transactions of Credit Card as a popular means of payment for public. With the tremendous frequency of transactions per minute worldwide, the multi-fold risk of fraudulent transaction has increased significantly for both the parties either user or issuer. This paper presents the comprehensive survey on multiple machine learning approaches to credit card fraud detection (CCFD). The existing approaches are eliciting good responses in terms of accuracy but the precocious Deep Learning algorithm (here, Convolutional Neural Network) was deployed in the anticipation of better accuracy. In this paper, comparative analysis has been carried out among various Machine Learning algorithms. Analytical parameters such as counts of layers, epochs & models have been employed. Outlandish outcome found for various machine learning classifier algorithms such as Random Forest, Support Vector Machine, K-Nearest Neighbor, Gaussian Naïve Bayes, Decision Tree, Logistic Regression, moreover, the dataset was fed to Convolutional Neural Network (CNN). The performance metrics for aforesaid classifiers in accordance with standard criteria was recorded. The best outcome was found with Random Forest Classifier depicting F1-score as 85.71%, Precision as 97.40%, and Accuracy as 99.96%.