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Credit Card Fraud Detection in Internet Using K-Nearest Neighbor Algorithm

Journal: IPASJ International Journal of Computer Science (IIJCS) (Vol.5, No. 11)

Publication Date:

Authors : ;

Page : 22-30

Keywords : Keywords: - Outlier Detection; Fraud Detection; K-Nearest Neighbor Algorithm; IP address;

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ABSTRACT Due to a rapid improvement in the electronic commerce technology, the utilize of credit cards has augmented. As credit card becomes the trendiest style of payment for individually online as well as habitual acquisition, luggage of credit card fraud also growing. Economic fraud is increasing radically with the development of modern technology and the global super highways of communication, consequential in the loss of billions of dollars worldwide each year. The falsified transactions are sprinkled with genuine transactions and simple pattern corresponding techniques are not often sufficient to detect those frauds accurately. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. Many modern techniques based on Artificial Intelligence, Data mining, Fuzzy logic, Machine learning, Sequence Alignment, Genetic Programming etc., has evolved in detecting various credit card fraudulent transactions. A KNN algorithm is an evolutionary search and optimization technique that Mimics natural evolution to find the best solution to a problem. Here the characteristics of credit card transactions undergo evolution to allow a modeled credit card fraud detection system to be tested. This method proves accurate in deducting fraudulent transaction and minimizing the number of false alert. If this algorithm is applied into bank credit card fraud detection system, the probability of fraud transactions can be predicted soon after credit card transactions.

Last modified: 2017-12-12 21:03:14