Abstract
As the world is evolving, the technological advancements radically transform our society and continue to reform the world. Credit card frauds are snowballing as payment requests and transactions happen mostly through the Internet. Recent fraudsters have a wide range of mediums and techniques they can use which makes each attack unique even if the same person is behind the attack. They leverage these techniques to portray standard user conduct (Amanze and Onukwugha in Int J Trend Res Dev 5:2394–9333, 2018 [1]). This type of fraud usually takes place when a purchase is made without authorization by either misusing the card that is stolen or lost or by making illegal transactions online or in person using your stolen PIN, CVV, or account number. In this paper, we use data mining techniques to predict fraudulent activities by analyzing unusual patterns and gather insights from transaction details. The aim is to accurately detect fraudulent activity by categorizing them as legal and illegal transactions. Such techniques and advanced security measures for credit card fraud detection are getting more elegant by the day and are making it more difficult for the fraudsters to accomplish their illegal activities.
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Kushal, K., Kurup, G., Urolagin, S. (2021). Data Mining Techniques for Fraud Detection—Credit Card Frauds. In: Gao, XZ., Kumar, R., Srivastava, S., Soni, B.P. (eds) Applications of Artificial Intelligence in Engineering. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-4604-8_10
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DOI: https://doi.org/10.1007/978-981-33-4604-8_10
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