A Comprehensive Machine Learning Approach to Credit Card Fraud Detection

Abstract

The financial sector is nevertheless dealing with a continuous and significant risk from credit card fraud, which requires the constant advancement of advanced systems for detecting and preventing fraudulent activities. This paper investigates machine learning applications for computational fluid dynamics in combustion. The paper includes a study of contemporary algorithms and findings obtained from comparative assessments. The ever-changing nature of fraudulent operations and the developing strategies used by evil individuals highlight the continual difficulties presented by credit card fraud. This article begins by analysing the credit card fraud environment, emphasizing the dynamic nature of fraudulent actions and the developing tactics used by harmful individuals. A thorough literature analysis explores the current research on CCFD, with a focus on the various machine learning approaches used in this field. The review covers both supervised and unsupervised learning methods, offering a comprehensive grasp of their individual advantages and disadvantages. Comparison of machine learning algorithms on benchmark databases yields important new information about their performance characteristics. One has to evaluate K-nearest neighbors, decision trees, random forests, logistic regression, Naive Bayes, and XGBoost together with accuracy, recall, precision, and F1 score measurements in order to fully understand the capabilities and limitations of algorithms. An analysis of the findings is given in the discussion section, together with significant discoveries and useful implications for the design and implementation of CCFD systems. The limitations and possible biases of these models are evaluated in-depth, offering insights into the challenges presented by imbalanced datasets, problems with data quality, and the choice of evaluation criteria. After evaluating the efficiency of every algorithm used, Random Forest has been shown to be the most accurate of them.

Keywords: CCFD, Machine learning, supervised learning, comparative analysis, future approaches, ethical issues