• Ankit Patel
• Yash Nanda
• Stevina Correia

The constant growth of different e-commerce platforms has contributed to an increase in the use of credit cards for payment purposes. An increase in credit card usage makes it vulnerable to hackers intending to exploit the system. As it becomes a popular method for payment purposes, a surge in fraud has been observed. Using the information captured from the transaction, numerous things can be deduced which might have detrimental effects. The current scenario requires efficient techniques for the detection of frauds which might reduce the cases of it which will directly impact the loss caused by it. Different techniques have been implemented for fraud detection and some of them show promising results in the detection of fraud. This paper shows the comparison between the accuracy of some supervised learning classification algorithms which are used for identifying fraudulent transactions.

anomaly, isolation forest, local outlier factor

"Credit Card fraud detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.753-755, June 2019, Available :http://www.jetir.org/papers/JETIR1908906.pdf

"Credit Card fraud detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp753-755, June 2019, Available at : http://www.jetir.org/papers/JETIR1908906.pdf