Predicting Credit Card Fraud Detection Using Machine Learning

Abstract

Credit card fraud remains a major threat to financial institutions because the increasing complexity of techniques employed threatens to overwhelm it. This work seeks to enhance fraud detection mechanisms by leveraging advanced machine learning frameworks to deliver improved and more efficient results. With a publicly available data set on Kaggle, we compare and contrast the performance of five algorithms: Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), Decision Trees, Random Forests, and a Stacking Classifier. CNNs are employed to reveal complex patterns in transactional data, whereas LSTM networks are employed for their capability in sequential and time-dependent behavior. Decision Trees and Random Forests both provide rule-based, structured classification, and Random Forests add ensemble learning for greater reliability as an extra benefit. The Stacking Classifier combines the two models' strengths in hoping that this will lead to greater overall predictive accuracy. In contrast, we hope to be able to determine the best method for real-time fraudulent transaction detection. The results of this research are anticipated to contribute to the creation of more secure, precise, and reliable credit card transaction systems—reducing financial loss and building consumer confidence in electronic financial services.