Credit Card Fraud Detection Using State Of The Art Machine Learning And Deep Learning Algorithms

Credit card fraud has emerged as a significant threat to financial security, prompting the urgent need for sophisticated detection mechanisms.Traditional rule-based systems are increasingly inadequate in identifying fraudulent patterns, especially with the advent of more complex and subtle fraud strategies.This study explores state-of-the-art machine learning and deep learning algorithms to enhance the accuracy and efficiency of credit card fraud detection systems.Various algorithms such as Random Forest, Gradient Boosting, Support Vector Machines, Long Short-Term Memory (LSTM) networks, and Convolutional Neural Networks (CNNs) are evaluated using a publicly available credit card transaction dataset.Emphasis is placed on the importance of handling class imbalance, which is a critical issue in fraud detection, by employing techniques like SMOTE and cost-sensitive learning.Feature engineering and data preprocessing play vital roles in improving model performance, and these are rigorously addressed in the methodology.Performance is assessed using metrics such as precision, recall, F1-score, and AUC-ROC to ensure balanced evaluation.The results demonstrate that deep learning models, particularly LSTM and CNNs, outperform traditional machine learning techniques in capturing temporal and spatial transaction patterns.However, the choice of algorithm may vary depending on the computational resources and the real-time nature of deployment.The proposed system integrates the most effective models into a hybrid framework that balances accuracy and efficiency.This research contributes to the ongoing development of intelligent, adaptive, and scalable fraud detection systems that can significantly reduce financial losses and enhance the security of credit card transactions.