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An integration of deep learning model with Navo Minority Over-Sampling Technique to detect the frauds in credit cards

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Abstract

In the real-world, the quality and quantity of various desirable products, services and facilities are effectively chosen by the people within a single step due to the rapid development of e-commerce technologies. Also, this technology offers scammers to do make credit card frauds (CCF), as credit card is considered as one of the most payment methods for any purchase. To prevent this fraudulent activities and payment losses, financial institution and researchers tries to develop an automated system for CCF Detection as CCFD. In this research work, an integration of is designed for detecting the CCF. Class inequality is presents in real-time dataset that leads poor performance of proposed classifier that needs to be addressed before final prediction. To address this issues, this research work develops a Navo Minority Over-Sampling Technique (NMOTe) to solve the class imbalance problem and increased the efficiency of CNN-GRU model. By using this developed model, huge financial losses are avoided by detecting the CCF. The experiments are carried out on three publicly available datasets in terms of important parameters to test the performance of proposed CNN-GRU model with existing deep learning (DL) classifiers. The results proved that the proposed model achieved 97.45% of accuracy and 99.80% of precision, where the existing CNN model achieved 93.22% of accuracy and 78.7% of precision on UCSD-FICO datasets.

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  1. Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India

    J. Karthika & A. Senthilselvi

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  1. J. Karthika
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  2. A. Senthilselvi
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Karthika, J., Senthilselvi, A. An integration of deep learning model with Navo Minority Over-Sampling Technique to detect the frauds in credit cards. Multimed Tools Appl 82, 21757–21774 (2023). https://doi.org/10.1007/s11042-023-14365-6

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