Fraudulent Transaction Detection and Prevention Using Deep Neural Networks

The objective of the project is to find out the best fraud detection model by comparing it with existing machine learning and deep learning techniques used for fraud detection. This study focuses on detecting fraudulent transactions using deep learning techniques, specifically Convolutional Neural Network (CNN) models.

A systematic literature review is conducted to address research questions, exploring six experimental steps for fraudulent transaction detection using deep learning. The integration of DFS improves fraud detection performance, resulting in a notable enhancement in recall, F1 score, and accuracy. Hyperparameter tuning, utilising grid search and random search algorithms yield remarkable improvements across precision, recall, and F1 score for the optimised CNN models compared to the baseline.