Credit Card Fraud Detection: A Machine Learning Approach

Abstract

The proliferation of credit card transactions in the digital era has exacerbated the threat of fraudulent activities, necessitating robust detection mechanisms. Traditional rule-based systems often fail to keep pace with the evolving nature of fraud, prompting the adoption of machine learning techniques. This research paper explores the efficacy of machine learning algorithms in detecting credit card fraud, aiming to enhance the security of financial transactions. Leveraging a comprehensive dataset comprising legitimate and fraudulent transactions, various machine learning models are trained, tested, and evaluated. Through meticulous experimentation and analysis, key insights are unearthed regarding the performance, strengths, and limitations of different algorithms in detecting fraudulent patterns. Furthermore, feature engineering techniques and ensemble learning methods are employed to improve detection accuracy and efficiency. The findings underscore the significance of adopting a holistic machine learning approach for credit card fraud detection, offering valuable insights for financial institutions and stakeholders in fortifying transaction security. Keywords: Credit card transactions, digital era, fraudulent activities, detection mechanisms, rule-based systems, credit card fraud.