Credit Card Fraud Detection Using Machine Learning Algorithms: A Theoretical Perspective

Credit card fraud poses a significant challenge to financial institutions worldwide, resulting in billions of dollars in annual losses. Traditional rule-based systems are static and struggle to adapt to evolving fraud patterns. In contrast, machine learning (ML) provides dynamic, data-driven solutions capable of detecting subtle and complex patterns in transaction data. This paper presents a comprehensive theoretical framework for credit card fraud detection using machine learning. It explores different algorithmic approaches, the importance of data preprocessing, feature selection, model evaluation, and strategies for dealing with imbalanced datasets. The paper emphasizes the strengths and limitations of various supervised learning models and ensemble methods in the context of fraud detection.