Call For Paper Volume:4 Issue:10 Oct'2017 |

Advancement in Fraud Detection Mechanism of Credit Application

Publication Date : 10/07/2015

Author(s) :

Mr. A. J. Shakadwipi.

Conference Name :
4th International Conference on Recent Trends in Engineering & Technology(ICRTET-2015) July 2-4,2015 Organized by SNJB's KBJ College of Engineering,Chandwad,Nashik,Maharashtra,India

Abstract :

Due to market uncertainties, declining economic growth and significant growth of online e-commerce makes fraud widespread. Rapid advancement in the electronic commerce technology, the use of credit cards has increased. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of credit card fraud also rising. The applications for these credit cards are based on internet or manual applications by the customers who require the smart cards and various loans. The applications in above cases found fraud and is a specific case of identity crime. Identity crime has emerged as a serious problem for credit card customers and banks. Fraudsters steal customer’s identity and obtain credit cards. This posses a major threat to the customers and banks. The existing non-data-mining detection system of business rules and scorecards, and known fraud matching has limitations. To address these limitations this paper provides an approach in identifying fraudsters at the time of application submission i.e. in real-time. The paper presents a new multi-layer fraud detection system based on data-mining algorithms. The detection system utilizes the two algorithms: communal detection (CD) and spike detection (SD) which complements each other, improving the fraud detection systems accuracy, time and cost. The credit card application undergoes validations at the time of submission before issuing the card. Better account for varying legal actions, and remove the redundant attributes and to store the fraudulent datum in blacklist using CBR algorithm. CBR algorithm analysis using retrieval, diagnosis and resolution to make the data more secure and to find the fraudulent data.

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