دانلود رایگان مقاله الزویر در مورد کاربرد داده کاوی در کشف کلاهبرداری در بیانیه مالی حسابرسی (کلیک کنید)
توضیحات :
وبسایت مکاله اقدام به ارائه پروژه ی ساینس دایرکت با فرمت pdf، از انتشارات الزویر برای رشته حسابداری، و با عنوان طبقه بندی هدایت تحقیقات در کاربرد داده کاوی برای کشف کلاهبرداری در بیانیه مالی حسابرسی، نموده است.
مشخصات این مقاله :
عنوان مقاله :
A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits
ترجمه فارسی عنوان :
طبقه بندی هدایت تحقیقات در کاربرد داده کاوی برای کشف کلاهبرداری در بیانیه مالی حسابرسی
سال انتشار : 2014
متعلق به مجله یا ژورنال : مجله بین المللی سیستم های اطلاعات حسابداری (International Journal of Accounting Information Systems)
فرمت: PDF
تعداد صفحات: 24
شماره پروژه: 5024
کلمات کلیدی :
Auditing, Fraud, Data mining
حسابرسی، تقلب، داده کاوی
چکیده :
Abstract
This paper explores the application of data mining techniques to fraud detection in the audit of financial statements and proposes a taxonomy to support and guide future research. Currently, the application of data mining to auditing is at an early stage of development and researchers take a scatter-shot approach, investigating patterns in financial statement disclosures, text in annual reports and MD&As, and the nature of journal entries without appropriate guidance being drawn from lessons in known fraud patterns. To develop structure to research in datamining, we create a taxonomy that combines research on patterns of observed fraud schemes with an appreciation of areas that benefit from productive application of data mining. We encapsulate traditional views of data mining that operates primarily on quantitative data, such as financial statement and journal entry data. In addition, we draw on other forms of data mining, notably text and email mining.
مقدمه این مقاله :
Introduction
This study explores the targeted application of data mining techniques to fraud detection as a core component of financial statement audits.1 Data mining refers to the extraction of knowledge from large volumes of data (Han and Kamber, 2006. 5). Data mining involves acquisition, loading and integration of
data; application of specialist data mining tools and, finally, human interpretation of the discovered meaning.2 The decision to incorporate data mining into financial audits is both a firm-level decision for accounting firms and an engagement-level decision. Firm-level decisions preclude engagement-level decisions in that if firm management does not see a beneficial reason to invest resources in software, infrastructure, training, and staffing then data mining will likely not be a cost-effective option for engagement teams. Larger accounting firms and some specialist providers offer a variety of data mining services. Currently, data mining is used in specialized audits (e.g., fraud audits or forensic audits) by expert staff in the professional services firms; however, data mining is seldom used in financial statement audits. When used, it is for identified high-risk clients by the firm’s data mining specialists. The majority of this paper is focused on engagement-level data mining activities; however, we revisit firm-level issues and research opportunities in the concluding parts of the paper.
توجه :
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