Optimized Credit Card Fraud Detection using Stacking and Grid Search Techniques for Enhanced Anomaly Detection

In this research paper presents novel machine learning based grid stack model to increase the robust functionality in detection fraud activity. Utilizing a curated dataset of transactional data, advanced models are applied and ensemble techniques such as stacking and grid search to enhance detection accuracy. This approach incorporates multiple algorithms, evaluating their performance to identify the best performing model, which achieved accuracies exceeding 95% in fraud detection. The proposed method has produced significantly perform on par with existing methodology in terms of accuracy of 95%, proving effective for high-stakes environments. The research highlights the application of stacking for improved detection performance, offering significant insights for financial institutions aiming to implement secure, efficient fraud detection systems. The paper presents the experimental methodology, evaluation metrics, dataset details, and concluding observations, providing a comprehensive view of model effectiveness and its implications for enhanced financial security.