Abstract
The problem of misclassification has always been a major concern in detecting online credit card fraud in e-commerce systems. This concern greatly poses a significant challenge to financial institutions and online merchants with regards to financial loss. This paper specifically compares an Artificial Neural Network trained by the Simulated Annealing technique (SA-ANN) with a proposed emerging online learning technology in anomaly detection known as the Hierarchical Temporal Memory based on the Cortical Learning Algorithms (HTM-CLA). Comparisons are also made with a deep recurrent neural technique based on the Long Short-Term Memory ANN (LSTM-ANN). The performances of these systems are investigated on the basis of correctly classifying credit card fraud (CCF) using an average classification performance ratio metric. The results of simulations on two CCF benchmark datasets (the Australian and German CCF data) showed promising competitive performance of the proposed HTM-CLA with the SA-ANN. The HTM-CLA also clearly outperformed the LSTM-ANN in the considered benchmark datasets by a factor of 2:1.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.