AN OVERVIEW FRAUD PREVENTION AND DETECTION TECHNIQUES. The modern information age is flooded with a rapidly growing and astonishingly huge amount of data. In the U.S alone, the total number of credit card transactions totaled 26.2 billion in 2012 5 . The processing of these data sets by banks and credit card issuers requires complex statistical algorithms to extract the raw quantitative data.
An overview of the processes that compose Knowledge Discovery in Databases (KDD) (Source: Fayyad et al, 1996) 6 .
These systems work by comparing the observed and collected data with expected values. Expected values can be calculated in a number of ways. For example, a behavior model would look at the way a customer’s bank account has been used in the past, and any deviance from usual purchasing habits would return a suspicion score. This method works by flagging a transaction with a typical score, usually between 1 and 999. The higher the score, the more suspicious the transaction is likely to be, or, the more similarities it shares between other fraudulent values.
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