Enhancing Credit Card Security with Machine Learning Fraud Detection

Abstract

Lastly, evaluating machine learning models in the context of credit card fraud 
detection and categorization can yield important insights into their performance 
across diverse settings. After looking at F-score, recall, accuracy, and precision 
metrics, it's evident that Random Forest consistently outperforms other models, 
showing how well it handles class imbalances. Random Forest can continue to 
perform well even in balanced datasets by utilizing oversampling strategies to 
achieve class balance. This makes it an even more effective model. Because of its 
adaptability and reliability, the model is thus ideal for application in actual fraud 
detection systems. The consistent performance of ensemble, Logistic Regression, 
and Gradient Boosting approaches in fraud detection tasks demonstrates the 
necessity of utilizing a variety of machine learning algorithms and oversampling 
tactics to increase classification performance. The effectiveness of Random Forest 
in minimizing class differences and the significance of a balanced training dataset 
are both highlighted by these results. In sum, this study's results will aid in the 
development of more reliable machine learning models for fraud detection, which 
in turn will have practical applications in domains such as finance. Future 
research could look into other optimization tactics and ensemble approaches to 
see whether they help the model perform better in real-world scenarios.