Credit Card Scam Detection Using State of Art Machine Learning and Deep Learning Algorithms

The widespread adoption of credit cards for online transactions has led to a significant rise in fraudulent activities, causing substantial financial losses for consumers and financial institutions. Traditional fraud detection systems often struggle to keep pace with the evolving tactics of fraudsters, necessitating the exploration of advanced detection methods. This study investigates the application of state-of-the-art machine learning and deep learning algorithms to enhance the accuracy and efficiency of credit card fraud detection. Utilizing a comprehensive dataset of anonymized credit card transactions, we evaluate various models, including Random Forest, Support Vector Machine, Gradient Boosting, and Convolutional Neural Networks (CNNs). Our findings indicate that deep learning techniques, particularly CNNs, outperform traditional machine learning models in identifying fraudulent transactions, achieving higher accuracy and reduced false positive rates. The study also emphasizes the importance of feature engineering, data preprocessing, and the handling of class imbalance to optimize model performance. Furthermore, we explore ensemble methods that combine multiple algorithms to leverage their individual strengths, resulting in improved detection capabilities. The proposed approach demonstrates significant potential for real-world implementation, offering a robust solution to mitigate the impact of credit card fraud. Future research will focus on integrating these models into real-time processing systems and exploring their adaptability to emerging fraud patterns.