Enhancing fraud detection in financial transactions using deep learning and imbalanced data handling techniques

This study explores new techniques to enhance fraud detection in the age of rapid financial transactions over the Internet. It focuses on utilizing deep learning methods to find complex fraud patterns, such as Deep NNs, RNNs, and autoencoders. The study uses SMOTE to effectively balance data in order to solve the problem of imbalanced datasets. The deep learning model is rigorously trained for binary classification using a large financial transaction dataset from Kaggle, showing excellent outcomes with high accuracy (0.99), precision (0.75), recall (0.85), and F1 score (0.80). The confusion matrix demonstrates that the model performs exceptionally well in reducing FP and FN. This study advances the area of fraud detection by proving the ability of deep learning and unbalanced data handling approaches to boost the accuracy and efficacy of fraud identification systems. As the amount of online financial transactions increases, strong fraud detection system becomes more important, and this study gives essential insights for developing this key subject.