Fraud Shield: Credit Card Fraud Detection with Ensemble and Deep Learning

Credit card fraud remains a persistent challenge, undermining financial security and trust. Traditional fraud detection systems struggle with the dynamic nature of fraudulent transactions, necessitating innovative approaches. Recent advancements introduce promising methodologies but face hurdles like data imbalance and evolving fraud patterns. This research study aims to elevate credit card fraud detection by integrating ensemble and deep learning models. Leveraging techniques like Voting, Stacking, CNN, and LSTM on the IEEE-CIS Fraud Detection dataset, we address challenges such as data imbalance and feature selection inefficiencies. The proposed approach, Fraud Shield, aims to fortify detection capabilities, acknowledging evolving fraud activities. By combining advanced techniques and a comprehensive dataset, our project provides a powerful tool for combating fraud in the financial sector. This study highlights the need for continual innovation in fraud detection and highlights the importance of collaboration between academia, industry, and regulatory bodies. Through comprehensive analysis and collaboration, this study paves way for a safer financial ecosystem resilient to emerging threats.