Financial fraud is increasing significantly with the development of modern technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. The companies and financial institution loose huge amounts due to fraud and fraudsters continuously try to find new rules and tactics to commit illegal actions. Thus, fraud detection systems have become essential for all credit card issuing banks to minimize their losses. The most commonly used fraud detection methods are Neural Network (NN), rule-induction techniques, fuzzy system, decision trees, Support Vector Machines
(SVM), Artificial Immune System (AIS), genetic algorithms, K-Nearest Neighbor algorithms.
The detection of fraud is a complex computational task and still there is no system that surely predicts any transaction as fraudulent. They just predict the likelihood of the transaction
to be a fraudulent.
The properties of a good fraud detection system are:
1) It should identify the frauds accurately
2) It should detecting the frauds quickly
3) It should not classify a genuine transaction as fraud