Applying Machine Learning to the Detection of Credit Card Fraud

Abstract

Credit card fraud is a global problem that costs both consumers and merchants a lot of money. To limit monetary losses and preserve credibility with clients, real-time detection of fraudulent transactions is essential. Due to their capacity to analyze vast amounts of transactional data and discover patterns suggestive of fraudulent behavior, machine learning methods have emerged as strong tools for credit card fraud detection. The goal of this research is to examine and evaluate the present status of machine learning algorithms for credit card fraud detection and their limitations. To do this, a thorough literature review on the use of machine learning to detect credit card fraud is conducted. The study's explanation of the research's findings is clear and drawbacks of existing methods, stressing both their contributions and potential for further development. Techniques for collecting data, dealing with missing numbers, outliers, and resolving class imbalance are also the subject of investigation. Methods for feature selection and engineering are investigated with the goal of improving machine learning model efficiency. Some of the machine learning techniques used in this research include: logistic regression, decision trees, random forests, support vector machines, artificial neural networks, gradient boosting methods, and ensemble approaches. The ROC curve, AUC, and confusion matrix are all measures of diagnostic accuracy. Metrics are explored in depth as assessment tools for determining how well these algorithms’ function. We offer empirical analyses and experimental findings that assess the efficacy of several machine learning algorithms on a sample dataset. Insights into the relative merits of various algorithms and its usefulness in identifying credit card fraud are provided in the discussion that follows the analysis of the data. In its final sections, the article addresses the challenges of credit card fraud detection and makes recommendations for future research. It stresses the need of addressing idea drift and changing fraud trends, including interpretability and explanation into fraud detection systems, and investigating new methods like deep learning and anomaly detection. In conclusion, our research contributes to the literature by providing a comprehensive evaluation of machine learning approaches to the detection of credit card fraud. Researchers, practitioners, and financial institutions may all acquire useful information from it to help them better identify and prevent fraud.