Enhancing Credit Card Fraud Detection: A Comparative Analysis of Anomaly Detection Models

Financial fraud poses a major threat to financial service institutions and clients, necessitating advanced anomaly detection capabilities. This paper delves into related deep learning models which can be be used to differentiate and detect fraudulent transactions from normal transactions and flag anomalies. Challenges include evolving attack strategies, class imbalance, and model interpret-ability. The proposed methods utilize of a synergistic combination of ensemble learning techniques and unsupervised deep neural networks, like autoencoders and recurrent neural networks. The outcomes exhibit reliable results on credit card transaction datasets. A comprehensive comparative analysis follows to compare the proposed approach to traditional fraud detection methods, demonstrating its superiority in accurately identifying fraudulent transactions, adapting to emerging threats, handling high-dimensional data, and mitigating class imbalances with respect to data privacy and security.