Data mining in computer auditing / Hiromi Wong, Siew Lan and Valery Fred Lee

• Auteurs principaux: Hiromi , Wong, Siew , Lan, Valery, Fred Lee
• Format: Thèse
• Publié: 2004
• Sujets: QA75 Electronic computers. Computer science; QA76 Computer software; T Technology (General)

In the past two decades, database technology has been evolved in order to turn the abundance of data into useful information. This evolution provokes in order to turn the abundance of data into useful information. This evolution provokes the emergence of data mining tools, which perform data analysis and extract data patterns. Data mining in computer auditing, these two businesses areas seem to be able to integrate. In which, auditing needs something to uncover patterns or speciousness while data mining can fulfill that need.This report will firstly over the introduction of data mining in computer auditing, project objective, data mining problems or issues, project scope, and expected outcome. The report will cover the literature review that started with an introduction to computer auditing, introduction to data mining, data mining techniques (classification, neural network and sequential analysis), and the existing data mining software. This report will explain the methodology that is used to develop this system, which is Waterfall Model with Prototyping. A research is conducted during the process of finding information that is related to this topic by examining the current data mining literature. The functional and non functional requirements for this project also will be discussed. Beside this, the development tools that will be uses to develop this project are also listed in the last chapter. In this thesis, we"ll also explain how our FRIDCA system is develop in the system analysis and design chapter. Also included how FRIDCA is an integrated system consisting of fraud detection and credit card approval is developed. We will explain further on how FRIDCA system can adapt to the ever evolving world of technology. However we also included the future enhancement of how FRIDCA can be applied in the real world.