Extended Centroid based Clustering Technique for Online Shopping Fraud Detection

Information Technology

with the enhancement in technology on-line banking like credit Card and Debit Card, Mobile Banking and most useful Internet Banking is the popular medium to transfer the money from one account to another is call customer to business. online Banking is gaining popularity day by day is very usefully for everyone, which increases the online transaction with the increase in online shopping, other charges, so the fraud cases related to this are also increasing and it puts a great burden on the economy, affecting both customers and company. It not only costs money, but also a great amount of time to restore the harm is all so done. The purpose is to prevent the customer from online transaction by using specific technique i.e. based on Data Mining. Customer Spending Behavior of product parching and payment transfer. The customer than spending behavior about the product that can be identified by searching approach by using K-Mean or k-Medoid clustering algorithm. If risk score is greater 0.75 then transaction is taken as to be fraudulent and then the security mechanism authenticates the user by entering the random number generate digit on the screen and the genuine user enters the code in a correct manner.

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