Fraud Detection in Credit Card Transactions: A Machine Learning Approach

Abstract

Credit card fraud poses a significant threat to financial institutions and consumers, leading to substantial financial losses annually. Traditional rule-based fraud detection systems often fall short in identifying novel fraudulent patterns. This paper explores the application of machine learning techniques to enhance fraud detection in credit card transactions. We delve into both supervised and unsupervised learning approaches, emphasizing the importance of feature engineering, data preprocessing, and model evaluation metrics. Additionally, we discuss the challenges associated with real-time fraud detection, adversarial attacks, and the ethical implications of deploying machine learning models in financial systems. Through comprehensive analysis and experimentation, we aim to provide insights into building robust and efficient fraud detection models.