Abstract
It is evident that the evolution in technology has surpassed expectations and reached different heights in a shorter span of time and with evolving technology; a lot of changes have been introduced in our lives, and one such change is the replacement of traditional payment methods with the credit card system. Credit card use increases the most during online shopping. With the huge demand for credit cards worldwide, credit card fraud cases to are increasing rapidly. In this paper, four machine learning algorithms that are decision tree, random forest, logistic regression, and Naïve Bayes have been used for training the models. Also, deep neural networks have been implemented for model training which is giving more promising results compared to the machine learning algorithms. The accuracy of each algorithm used in the implementation of the credit card fraud detection has been compared and analyzed.
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Gorte, A.S., Mohod, S.W., Keole, R.R., Mahore, T.R., Pande, S. (2023). Credit Card Fraud Detection Using Various Machine Learning and Deep Learning Approaches. In: Gupta, D., Khanna, A., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Lecture Notes in Networks and Systems, vol 492. Springer, Singapore. https://doi.org/10.1007/978-981-19-3679-1_52
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DOI: https://doi.org/10.1007/978-981-19-3679-1_52
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