Abstract
The occurrence of Credit card fraud is increasing at an unprecedented rate. Individuals and financial organizations are considering credit card fraud as a morally reprehensible criminal act. Both individuals and financial organizations experience significant financial losses annually, totaling billions of dollars. This research study intends to utilize machine learning approaches to detect the instances of fraudulent credit card transactions. The objective of the proposed approach is to reduce the likelihood of gaining fraudulent access to the consumer accounts. The assessment methods are carried out by utilizing different metrics such as accuracy, sensitivity, specificity, and precision. Based on the data obtained, it is evident that the Random Forest algorithm demonstrates the utmost level of 98.6% accuracy.
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Kandula, A.R., Kaza, P.T., Nancharla, S.C., Vemuri, G.T., Gudupudi, V.K., Tadi, S.K. (2024). Comparative Examination of Credit Card Fraud Detection Using Machine Learning Algorithms. In: Asirvatham, D., Gonzalez-Longatt, F.M., Falkowski-Gilski, P., Kanthavel, R. (eds) Evolutionary Artificial Intelligence. ICEASSM 2017. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-8438-1_17
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DOI: https://doi.org/10.1007/978-981-99-8438-1_17
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