FraudMiner: A Novel Credit Card Fraud Detection Model Based on Frequent Itemset Mining

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ID: 104891
2014
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Abstract
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.
Reference Key
seeja2014fraudminerthe Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Seeja, K. R.;Zareapoor, Masoumeh;
Journal the scientific world journal
Year 2014
DOI
DOI not found
URL
http://dx.doi.org/10.1155/2014/252797
Keywords
chemistry Biology (General) Medicine Technology (General) Engineering (General). Civil engineering (General) Technology Science (General) Science Medical technology physics telecommunication mathematics electronic computers. computer science

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