Credit Card Fraud Detection Using Machine Learning Algorithms

Abstract

This study aims to address the rising issue of credit card fraud by developing a machine learning model capable of identifying and preventing fraudulent transactions. The model works by analyzing transaction data to detect potential fraud, subsequently canceling the transaction and alerting the credit card owner. Credit card fraud detection is a classification problem, where various machine learning algorithms are applied to distinguish between legitimate and fraudulent transactions. The analysis emphasizes the importance of robust countermeasures due to the increasing use of credit cards globally. However, real-world implementation of such systems may face challenges, particularly in securing the cooperation of banks and addressing resource constraints. The study also highlights key dataset features that correlate with fraudulent behavior, with ensemble methods standing out as top-performing algorithms in terms of accuracy and efficiency.