Özet:

Credit card payment is one of the most preferred methods of e-commerce sites. Fraud orders are the biggest concerns for online shopping sites. Fraud operations affect not only customers but also companies and banks. Hence, companies should be able to classify orders and take measures against suspicious transactions. Classification is easier on the banking side because of more information about customers, but it is more difficult to determine this process on e-commerce sites. In this study, the actual order data of a private e-commerce enterprise was examined and suspicious transactions were determined. First of all, all order data was analyzed and filtered. The best variables for classification were determined by variable selection algorithms. Afterwards, classification algorithms were applied and suspicious orders were determined with 92% success rate. Naïve Bayesian, Decision Trees and Artificial Neural Network were used as comparative data mining methods.

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