online credit card fraud detection


Credit card fraud events take place frequently and then result in huge ?nancial losses. Criminals can use some technologiessuchasTrojanorPhishingtostealtheinformationof other peoplescredit cards. Therefore an ef?ctivefraud detection method is important since it can identify a fraud in time when a criminal uses a stolen card to consume. One method is to make full use of the historical transaction data including normal transactions and fraud ones to obtain normal/fraud behavior features based on machine learning techniques and then utilize these features to check if a transaction is fraud or not. In this paper two kinds of random forests are used to train the behaviorfeaturesofnormalandabnormaltransactions.Wemake a comparison of the two random forests which are different in their base classi?ers and analyze their performance on credit fraud detection. The data used in our experiments come from an e-commerce company in China



SVM-naïve bayes-random forest-DNN-Multinomial Logistic Regression


machine learning


₹12000 (INR)


IEEE 2018