RNN-BD: an approach for fraud visualisation and detection using deep learning
by G. Madhukar Rao; K. Srinivas
International Journal of Computational Science and Engineering (IJCSE), Vol. 25, No. 2, 2022

Abstract: The evolution of banking information systems considerably increases fraud activities, which can have a negative impact on banking financial services. The use of credit cards has increased significantly due to electronic funding, electronic services and e-commerce activities. Massive amounts of data from credit card transactions can result in big data. Researchers are now using machine learning algorithms to detect and analyse fraud in online transactions. One of the major concerns of the banking industry is the visualisation and detection of credit card fraud. Machine learning techniques only work well when the dataset is small and does not have complex models. Deep learning, on the other hand, processes large and complex data sets. The objective of this paper is to visualise and detect credit card fraud by incorporating deep learning and dimensionality reduction techniques. A real dataset is used to assess the effectiveness of the intended work. The results show that our proposed model is more efficient in identifying fraudulent transactions to reduce fraud and income loss. We found that our deep learning model can be used to identify fraudulent transactions and reduce fraud losses to protect customer interests.

Online publication date: Tue, 12-Apr-2022

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