Amanze B.C., Onukwugha C.G.
Since the evolution of the internet, many small and large companies have moved their businesses to the internet to provide services to customers worldwide. Credit-card fraud is increasingly rampant in the recent years for the reason that the credit-card is majorly used to request payments by these companies on the internet. Therefore the need to ensure secured transactions for credit-card owners when consuming their credit cards to make electronic payments for goods and services provided on the internet is a criterion. Data mining has popularly gained recognition in combating credit-card fraud because of its effective, and machine learning algorithms that can be implemented to detect or predict fraud through Knowledge Discovery from unusual patterns derived from gathered data. This system implements the unsupervisedanomaly detection algorithm of data mining to detect fraud in a real time transaction on the internet, and thereby classifying the transaction as legitimate, suspicious fraud and illegitimate transaction. The anomaly detection algorithm is designed on the data mining technique which implements the working principal of the human brain. To understand how credit card fraud are being committed, in this study the different types of fraudsters that commit online credit card fraud and the techniques used by these online fraudsters to commit fraud on the internet is discussed.
Credit card fraud, fraudsters, and data mining