صورتهای مالی گروه های تجاری
ترجمه نشده

صورتهای مالی گروه های تجاری

عنوان فارسی مقاله: تشخیص تقلب برای صورتهای مالی گروه های تجاری
عنوان انگلیسی مقاله: Fraud detection for financial statements of business groups
مجله/کنفرانس: مجله بین المللی سیستم های اطلاعات حسابداری - International Journal Of Accounting Information Systems
رشته های تحصیلی مرتبط: حسابداری
گرایش های تحصیلی مرتبط: حسابداری مالی، حسابداری دولتی، حسابرسی
کلمات کلیدی فارسی: کشف تقلب، گروه تجاری، صورتهای مالی، متن کاوی
کلمات کلیدی انگلیسی: Fraud detection، Business group، Financial statement، Texting mining
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.accinf.2018.11.004
دانشگاه: Department of Accounting and Information Systems, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan, ROC
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 2/645 در سال 2018
شاخص H_index: 44 در سال 2019
شاخص SJR: 0/478 در سال 2018
شناسه ISSN: 1467-0895
شاخص Quartile (چارک): Q2 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 23
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E13265
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Design of a fraud detection process for financial statements of business groups


3- Development of fraud detection techniques for financial statements of business groups


4- Demonstration and evaluation of the proposed approach


5- Conclusions


References

نمونه متن انگلیسی مقاله

Abstract


Investors rely on companies' financial statements and economic data to inform their investment decisions. However, many businesses manipulate financial statements to raise more capital from investors and financial institutions, which reduces the practicality of financial statements. The modern business environment is highly information-oriented, and firms' information systems and activities are complex and dynamic. Technology for avoiding fraud detection is continually updated. Recent studies have focused on detecting financial statement fraud within a single business, but not within a business group. Development of methods for using diverse data to detect financial statement fraud in business groups is thus a high priority in the advancement of fraud detection.
This study develops an approach for detecting fraud in the financial statements of business groups. The proposed approach is applied to reduce investment losses and risks and enhance investment benefits for investors and creditors. The study objectives are achieved through the following steps: (i) design of a process for detecting fraud in the financial statements of business groups, (ii) development of fraud detection techniques for use with such statements, and (iii) demonstration and evaluation of the proposed approach.


Introduction


Since the Procomp case and Enron event, investors, governments, and regulatory authorities have begun to focus on financial statement fraud committed by business groups. Falsified financial statements may result in large losses for investors and creditors in capital markets. The modern business environment is highly information-oriented, and firms' systems and activities are complex and dynamic. Technology used to avoid fraud detection is constantly updated. Development of methods for using diverse data to detect financial statement fraud in business groups is thus a high priority in the advancement of fraud detection. Various approaches have been developed to detect fraud in corporate financial statements. Kirkos et al. (2007) explored the effectiveness of data mining (DM) classification techniques for detecting firms' fraudulent financial statements (FFS) and identified factors associated with FFS. Auditors assisted in detecting fraud using DM techniques. The study also investigated the usefulness of decision trees, neural networks, and Bayesian belief networks in identifying FFS. Ravisankar et al. (2011) also used DM techniques such as multilayer feed forward neural network (MLFF), support vector machine (SVM), genetic programming (GP), group method of data handling (GMDH), logistic regression (LR), and probabilistic neural network (PNN) to identify companies that had committed financial statement fraud. Each of these techniques was tested on a dataset covering 202 Chinese companies, and the results of tests with and without feature selection were compared. Among the techniques, PNN was the most accurate without feature selection, and GP and PNN were the most accurate with feature selection (with marginally equal accuracies).

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