APPLICATIONOF DATA MINING : FRAUD DETECTIONAbstractTheapplication of Data Mining techniquesfor the detection of Financial Fraud:TheData Mining techniques in the field of Fraud Detection was analyzed and categorized intofour categories of financial fraud (securities and commodities fraud, insurance fraud, bank fraud, andother related financial fraud) and six classes of data mining techniques (regression, classification, clustering, outlier detection, prediction, andvisualization). TheData Mining approach have been applied wide-ranging to the detection of creditcard fraud, although insurance fraud and cooperate fraud have also attracted askillful trade of attention in recent years. In difference, we find a distinctlack of research on money laundering, securities and commodities fraud and mortgage fraud. The major data miningtechniques used for FFD are neural networks, logistic models, decision trees,and the Bayesian belief network, all of which them provide key solutions to theproblems inherited in the detection and in the classification of fraudulentdata.Introduction”The Associationof Certified Fraud Examiners (ACFE) defined fraud as: The use of one’sprofession for personal enrichment through the deliberate misuse or applicationof the employing organization’s resources or assets.” In the technologicalsystems, fraudulent activities have occurred in many areas of daily life suchas mobile communications, telecommunication net works, E-commerce and onlinebanking.
Fraud detection includes identification of fraud as fast as possibleonce it has been accomplished. Fraud detection methods are continuouslydesigned to defend criminals in reinventing to their strategies. The developmentof new fraud detection methods is made more difficult due to the severelimitation of the exchange of ideas in fraud detection. At current, fraud detectionhas been applied by a number of methodology such as artificial intelligence, statisticsand data mining.