Typically, measures taken to combat fraud can be distinguished into two categories:
PREVENTION. Fraud Prevention constitutes the necessary steps to prevent fraud from occurring in the first place. In the credit space, fraud prevention starts with the application process and organizations are using preventative methods used to deter fraudsters, such as MasterCard SecureCode and Verified by Visa.
DETECTION. Fraud Detection, the focus of this report, comes into play once fraud prevention fails. Detection consists of identifying and detecting the fraudulent activity as quickly as possible and implementing the necessary methods to block and prevent the card from being used by the perpetrator again 7 .
The techniques used to detect fraud also fall into two primary classes: Statistical techniques (clustering, algorithms) and Artificial Intelligence (ANN, FNN, Data Mining) 8 . Both of these methods still involve mining through the available data and highlighting any anomalies (which can be defined by a set of rules) from the purchasingand transaction data of the customer. The difference is that where we used human analysts to manually search useable knowledge in the past, today we make use by machine learning 9 10 . Issues arise when criminals change their tactics to adapt to a prevention method that is in place, therefore more intelligent and sophisticated technology which ‘learns’ is essential for the detection of fraud.
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