The Use of Data Mining Techniques as a Complementary Audit Method

Author:Adrian VINTILESCU BELCIUG, Ph. D. Student, Daniela CREŢU, Ph. D. Student, Carmen GEGEA, Ph. D. Student

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Keywords:audit, data mining, knowledge, data base, true and fair view

Abstract:
Data mining, known as „the process of discovering knowledge from huge data bases”, represents a modern and powerful instrument that can be used for extracting useful information, but still hidden or unexpected, in order to be analyzed.\r\nThe use of data mining in audit area provides increasing quality of its missions, relating to the identification of risks, the scene intervention and fraud detection. \r\nThe purpose of this article is to present the impact of using data mining techniques in financial audit (as extracting knowledge tool), by auditing a real financial statement.\r\nThis paper approaches CRISP-DM methodology by presenting a specific data mining model (“if-then” association rules) and a data base regarding the acquisition list of an entity (dignified with “401” account). The purpose of this paper is finding prior useful patterns within the data base.\r\nThis study confirmed the usefulness of this demarche by unattended identification of some tests which must be done and by some risks identification which can be evaluated.\r\nThis paper brings to light a new audit mission approach, complementary with the common risks analyzing methods and on scene intervention, by using data mining techniques as a tool.\r\n

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