Analysis of Different Machine Learning Models for Credit Card Fraud Detection

The increase in number of online transactions has led to a significant amount of credit card fraud over the past decade. Unauthorized use of one’s credit card information by stealing the information through dark web or scam calls, poses a major risk to both customer and businesses, particularly in e-commerce setting. This paper presents a comparative analysis of multiple machine learning models for credit card fraud detection, including logistic regression, isolation forest, K – mean clustering, and convolutional neural networks. With a highly unbalanced dataset we aim to evaluate these models’ performance in differentiating between genuine and fraudulent transactions based on features such as transaction history, user details, and merchant information. Our experiment results will help provide insights into effectiveness of each model for finding patterns to distinguish between real and fake that can be applied to real world data. This research contributes to the field of financial security by offering guidance on model selection for credit card fraud detection and related applications. View this project here.