Data Mining techniques for the detection of fraudulent financial statements - دانلود رایگان
دانلود رایگان
دانلود رایگان Data Mining techniques for the detection of fraudulent financial statements
تکنیک های داده کاوی برای
کشف تقلب در صورت های مالی
Data Mining techniques for the detection of fraudulent
financial statements
لینک دانلود مقاله:
http://s8.picofile.com/file/8315120284/22094.pdf.html
1.
مقدمه
چکیده
این مقاله کارایی تکنیک
های طبقه بندی با استفاده از داده کاوی (DM)در شناسایی
شرکت هایی را که صورت های مالی خلاف واقع (FFS) منتشر می
کنند، بررسی کرده و به شناسایی عوامل مرتبط با FFS نیز می پردازد. حسابرسان می توانند با بهره بردن از تکنیک های
داده کاوی برای کشف تقلب های مدیریتی، کار خود را ساده تر کنند. این مطالعه تأثیر استفاده
از درخت های تصمیم گیری، شبکه های عصبی و شبکه های بیزین در شناسایی صورت های مالی
تقلبی را بررسی می نماید. بردار درون داد از نسبت های برگرفته از صورت های مالی
تشکیل شده است. سه مدل مذکئور از لحاظ عملکرد مورد مقایسه قرار می گیرند.
کلمات کلیدی: صورت های مالی تقلبی، تقلب مدیریتی، داده کاوی، حسابرسی،
یونان
امروزه حسابرسی (ممیزی) به کاری بسیار چالش بر انگیز
بدل شده و شواهد زیادی موجودست مبنی براین که انجام فعالیت های مربوط به «دستکاری
در حساب ها» به شدت رواج دارد. کاسکیوارا سال 2002 را از نظر دستکاری در حساب ها،
«سالی هولناک» نامیده و ادعا می کند که این دستکاری ها هنوز هم ادامه دارد
(کاسکیوارا، 2004). برخی از برآوردها حاکی از آنند که تقلب و کلاهبرداری سالانه
بیش از 400 میلیارد دلار برای ایالات متحده هزینه دربردارد (ولز، 1997). اسپاتیس،
دامپوس و زاپونیدیس (2002) مدعی این هستند که تقلب در صورت های مالی ظرف چند سال
اخیر به شدت رواج گرفته است. تقلب مدیریتی به معنای کلاهبرداری عامدانه از سوی
مدیریت است به نحوی که ارائه صورت های مالی ساختگی گمراه کننده باعث خسارت دیدن
سرمایه گذاران و بستانکاران شود. حسابرسان می بایست حتما در طول فرایند ممیزی،
احتمال وقوع تقلب مدیریتی را برآورد کنند. سازمان AICPA صراحتا
به مسئولیت حسابرسان در کشف تقلب اذعان دارد (کالینان و ساتن، 2002). حسابرسان
برای گسترش پیش بینی ه
Data
Mining
techniques
for
the
detection
of
fraudulent
financial
statements
مقاله
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فایل فلش
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اقدام پژوهی
درس پژوهی
جزوه
خلاصه
Data Mining techniques for the detection of …
Forecasting Fraudulent Financial Statements using Data Mining by S. Kotsiantis , E. Koumanakos, D. Tzelepis, V. Tampakas , 2006 This paper explores the effectiveness of machine learning techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS.
Data Mining techniques for the detection of fraudulent ...
Data Mining techniques for the detection of fraudulent financial statements. This paper explores the effectiveness of Data Mining (DM) classification techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS.
The Efficacy of Predictive Methods in Financial …
The existence and persistence of financial statement fraud (FSF) are detrimental to the financial health of global capital markets. A number of detective and predictive methods have been used to prevent, detect, and correct FSF, but their practicability has always been a big challenge for researchers and auditors, as they do not address real-world problems.
Detecting Fraudulent Financial Statements for the ...
Previous studies have reported the superior classification performance of data mining techniques over traditional statistical methods [1–7,9–20]. The literature related to FFS detection driven by data mining techniques is presented in Table1. Table 1. Research on data mining techniques in detecting fraudulent financial statements (FFS). Sl.
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describes classification of data mining techniques and applications for financial accounting fraud detection. Section 3 provides distribution of the research literature as per the applications and techniques of data mining for the detection of financial accounting fraud. Section 4 describes our framework in more detail.
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This paper explores the effectiveness of Data Mining (DM) classification techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS. In accomplishing the task of management fraud detec-tion, auditors could be facilitated in their work by using Data Mining techniques. This study investigates the usefulness of Decision
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describes classification of data mining techniques and applications for financial accounting fraud detection. Section 3 provides distribution of the research literature as per the applications and techniques of data mining for the detection of financial accounting fraud. Section 4 describes our framework in more detail.
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used techniques for prevention and detection of financial frauds. The implementation of data mining techniques for fraud detection follows the traditional information flow of data mining, which begins with feature selection followed by representation, data collection and management, pre - processing, data mining, post-processing, and performance
A Review of Financial Accounting Fraud Detection based on ...
describes classification of data mining techniques and applications for financial accounting fraud detection. Section 3 provides distribution of the research literature as per the applications and techniques of data mining for the detection of financial accounting fraud. Section 4 describes our framework in more detail.
Detection of fraudulent financial statements …
The research objects are companies which experienced both fraudulent and non-fraudulent... The purpose of this study is to construct a valid and rigorous fraudulent financial statement detection model. ... Detection of fraudulent financial statements using the hybrid data mining approach.
Data Mining Tools To Detect Financial Fraud
used techniques for prevention and detection of financial frauds. The implementation of data mining techniques for fraud detection follows the traditional information flow of data mining, which begins with feature selection followed by representation, data collection and management, pre - processing, data mining, post-processing, and performance
Detection of fraudulent financial statements …
The research objects are companies which experienced both fraudulent and non-fraudulent... The purpose of this study is to construct a valid and rigorous fraudulent financial statement detection model. ... Detection of fraudulent financial statements using the hybrid data mining approach.
Detection of fraudulent financial statements …
The research objects are companies which experienced both fraudulent and non-fraudulent... The purpose of this study is to construct a valid and rigorous fraudulent financial statement detection model. ... Detection of fraudulent financial statements using the hybrid data mining approach.
Data mining techniques for the detection of …
(PDF) Data mining techniques for the detection of fraudulent financial statements | Yannis Manolopoulos - Academia.edu This paper explores the effectiveness of Data Mining (DM) classification techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS.
Data mining techniques for the detection of …
(PDF) Data mining techniques for the detection of fraudulent financial statements | Yannis Manolopoulos - Academia.edu This paper explores the effectiveness of Data Mining (DM) classification techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS.
Fraud Detection on Financial Statements Using Data Mining ...
Fraud Detection on Financial Statements Using Data Mining Techniques Murat Cihan Sorkun*1,2, Taner Toraman2 Accepted : 12/06/2017 Published: 30/09/2017 DOI: 10.1039/b000000x 1 Abstract:This study explores the use of data mining methods to detect fraud for on e-ledgers through financial statements. For this
Researches of Detection of Fraudulent Financial Statements ...
explored. Our study investigates the usefulness of Data Mining techniques including Decision Trees, Neural Networks and Bayesian Belief Networks in the identification of fraudulent financial statements. At last, we compare the three models in terms of their performances. Keywords:Data mining, Fraudulent financial statements, Decision tree. 1.