Detection of credit card fraud using a deep learning Autoencoder model combined with the isolation forest algorithm

Abstract

Credit card fraud is one of the issues that many banks and organizations are concerned about
and researching. Building systems to monitor and detect fraud is extremely necessary and urgent for
banks, to minimize the loss of money without clear reasons leading to losses, and also to affect the
reputation and credibility of the bank or credit organization. Automating credit card fraud detection is
a perfect way to meet user needs and provide security on a large scale. Based on that, this paper has
researched, experimented, and proposed a new model that combines two algorithms, Autoencoder and
Isolation Forest, on two sample datasets simulating bank transactions. Using deep learning research
methods, the proposed model achieved nearly absolute efficiency of up to 99.77%, significantly improving
accuracy compared to previous studies