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APPLICATION
OF DATA MINING : FRAUD DETECTION

Abstract

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The
application of Data Mining techniques
for the detection of Financial Fraud:
The
Data Mining techniques in the field of Fraud Detection was analyzed and categorized into
four categories of financial fraud (securities and commodities fraud, insurance fraud, bank fraud, and
other related financial fraud) and six classes of data mining techniques (regression, classification, clustering, outlier detection, prediction, and
visualization). The
Data Mining approach have been applied wide-ranging to the detection of credit
card fraud, although insurance fraud and cooperate fraud have also attracted a
skillful trade of attention in recent years. In difference, we find a distinct
lack of research on money laundering, securities and commodities fraud  and mortgage fraud. The major data mining
techniques used for FFD are neural networks, logistic models, decision trees,
and the Bayesian belief network, all of which them provide key solutions to the
problems inherited in the detection and in the classification of fraudulent
data.

Introduction

“The Association
of Certified Fraud Examiners (ACFE) defined fraud as: The use of one’s
profession for personal enrichment through the deliberate misuse or application
of the employing organization’s resources or assets.” In the technological
systems, fraudulent activities have occurred in many areas of daily life such
as mobile communications, telecommunication net works, E-commerce and online
banking. Fraud detection includes identification of fraud as fast as possible
once it has been accomplished. Fraud detection methods are continuously
designed to defend criminals in reinventing to their strategies. The development
of new fraud detection methods is made more difficult due to the severe
limitation of the exchange of ideas in fraud detection. At current, fraud detection
has been applied by a number of methodology such as artificial intelligence, statistics
and data mining.

 

 

 

 

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