Employing Artificial Intelligence to Minimize Internet Fraud

Internet fraud is increasing on a daily basis with new methods for fraudulently extracting funds from governments, corporations, businesses, and ordinary people appearing almost hourly. The increasing use of on-line purchasing and the constant and sometimes ineffective vigilance of both seller and buyer seemingly lead to the conclusion that the criminal seems to be one-step ahead at all times. Today, pre-empting or preventing fraud before it happens occurs in the manual, non-computer based business transactions because of the natural intelligence of both seller and buyer. Currently, even with advances in computing techniques, near human levels of intelligence is not the strength of any computing system, yet techniques are available which may reduce the occurrences of fraud, and are usually referred to as artificial intelligence systems. This paper provides an overview of the use of current artificial intelligence (AI) techniques as a means of combating transaction fraud. Initially this paper describes how artificial intelligence techniques are employed in systems for detecting credit card fraud (online and offline) and insider trading within the Bourses. Following this, an attempt is made to propose using the MonITARS (Monitoring Insider Trading and Regulatory Surveillance) Systems framework which uses a combination of genetic algorithms, neural nets and statistical analysis in detecting insider dealing, to be used in the detection of transaction fraud. Finally, the paper discusses future research agenda to the role of using MonITARS-type systems.

並列關鍵字

Artificial Intelligence ； Internet Fraud ； Networking ； MonITARS Systems

國際替代計量

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