Abstract
The finance sector has a major role to play in present-day generation, where almost every individual has to deal with banking either physically or online. New advances in e-commerce have made the credit card the easiest mode of payment, thus showing steep rise in fraudulent transaction as well. This is now a major point of concern as it can result in an annual loss of billion of dollars to banking sector. With the advent of Internet, technology studies are conducted that utilize various machine learning models to detect and analyze fraud. Therefore, this leads to the development of efficacious model for the prediction of fraudulent transactions. This paper analyzes the standard models of machine learning and then boosting algorithms (ensemble technique) are analyzed to find out which perform more accurately and precisely to predict fraudulent transaction. On analyzing the outcome, it was examined that boosting algorithms gives better results.
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Zainab, K., Dhanda, N., Abbas, Q. (2021). Analysis of Various Boosting Algorithms Used for Detection of Fraudulent Credit Card Transactions. In: Kaiser, M.S., Xie, J., Rathore, V.S. (eds) Information and Communication Technology for Competitive Strategies (ICTCS 2020). Lecture Notes in Networks and Systems, vol 190. Springer, Singapore. https://doi.org/10.1007/978-981-16-0882-7_98
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DOI: https://doi.org/10.1007/978-981-16-0882-7_98
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