Credit Card Fraud Detection

With the increasing reliance on digital transactions, credit card fraud has become a significant challenge for financial institutions. Traditional fraud detection methods struggle to keep up with evolving fraudulent tactics, necessitating advanced machine learning-based solutions. Study explores the effectiveness of multiple machine learning algorithms, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Decision Trees, Random Forests, and a Stacking Classifier, in identifying fraudulent transactions. The research utilizes a Kaggle-sourced dataset to train and evaluate these models. CNNs effectively capture intricate transaction patterns, while LSTM networks analyze sequential dependencies. Decision Trees and Random Forests enhance classification accuracy through hierarchical decision-making and ensemble learning. The Stacking Classifier integrates multiple models to optimize overall zperformance. Comparative analysis of these techniques reveals that the Stacking Classifier achieves the highest accuracy, making it a promising approach for real-time fraud detection. The study’s findings contribute to the development of more robust fraud detection frameworks, reducing financial losses and strengthening consumer trust. Key Words: Credit Card Fraud Detection, Machine Learning, CNN, LSTM, Stacking Classifier, Random Forest, Fraud Prevention