Credit Card Fraud Detection Using Machine Learning Algorithms

Abstract

Abstract: The study delves into the prevalent issue of credit card fraud within electronic transactions. Employing machine learning techniques, including logistic regression, the research focuses on the development and evaluation of fraud detection models. The dataset undergoes comprehensive pre-processing steps, addressing common issues such as missing data and class imbalance. Various machine learning algorithms are explored, with a specific emphasis on logistic regression. Sampling techniques are employed to balance the dataset, ensuring equal representation of legitimate and fraudulent transactions. Model evaluation involves metrics like accuracy, precision, recall, and F1 score to assess performance. The outcomes shed light on the logistic regression model's effectiveness in detecting fraudulent transactions. The study also outlines inherent limitations, acknowledging challenges such as imbalanced datasets and the dynamic nature of fraud tactics. In summary, this research contributes to the ongoing advancements in credit card fraud detection, utilizing machine learning for enhanced security in electronic transactions. The discussed findings and limitations serve as a valuable foundation for future developments in this critical field.