This paper proposes an intelligent credit card fraud detection model for detecting fraud from highly imbalanced and anonymous credit card transaction datasets. The class imbalance problem is handled by finding legal as well as fraud transaction patterns for each customer by using frequent itemset mining. A matching algorithm is also proposed to find to which pattern (legal or fraud) the incoming transaction of a particular customer is closer and a decision is made accordingly. In order to handle the anonymous nature of the data, no preference is given to any of the attributes and each attribute is considered equally for finding the patterns. The performance evaluation of the proposed model is done on UCSD Data Mining Contest 2009 Dataset (anonymous and imbalanced) and it is found that the proposed model has very high fraud detection rate, balanced classification rate, Matthews correlation coefficient, and very less false alarm rate than other state-of-the-art classifiers.
K. Seeja and m. Zareapoor REF proposed an intelligent model that can efficiently detect credit card fraud of data sets of anonymous and highly unbalanced transactions for credit card transactions using frequent extraction of sets of elements.
FraudMiner: A Novel Credit Card Fraud Detection Model Based on Frequent Itemset Mining
The Scientific World Journal
The Scientific World Journal
Medicine