improving identity crime detection using scheduling for fast response multi-pattern matching over streaming events
G. Gunasekaran,Mareeswari.V
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:3 Issue:1 Year: 26 June,2014 Pages:375-385
Abstract
Due to a rapid advancement in the electronic commerce technology, the use of credit cards has dramatically increased. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. Credit card fraud is the specific crime in banking system. The credit card crime has been growing rapidly for the last few years. The process of making profit through credit card in the economy has been decreased about 8.2 crore annually in India. To avoid and predict the fraudulent activities on credit card application, in this paper a method of detecting the fraud over credit card on behalf of the cibil score. As datamining provides various ways to retrieve an appropriate data from the storage, here in proposed system an efficient way of matching the data provided by the applicants of credit card along with the cibil list to predict the fraudsters. The existing process of fraud detection has the drawbacks of effectiveness and scalability for multiple variants of data, the Scheduling for Fast Response multi-pattern matching algorithm used to match the large amount of attributes, In order to predict the fraudulent applicants with an appropriate time constraints. Together with the communal detection (CD) and spike detection (SD) algorithm that removes the redundant attributes and generates the credit score for cibil list or black list. The cibil score varies about 300 to 900, it has been recorded in the credit history and by considering its range the credits are provided to the lender or customer, and they are added to the white list.
Kewords
Data mining-based fraud detection, security, data stream mining, anomaly detection, Event-based (EBS) and Run-based (RBS) Scheduling.
Reference
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