مقاله انگلیسی رایگان در مورد تشخیص تقلب برای صورتهای مالی گروه های تجاری – الزویر ۲۰۱۹
مشخصات مقاله | |
ترجمه عنوان مقاله | تشخیص تقلب برای صورتهای مالی گروه های تجاری |
عنوان انگلیسی مقاله | Fraud detection for financial statements of business groups |
انتشار | مقاله سال ۲۰۱۹ |
تعداد صفحات مقاله انگلیسی | ۲۳ صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
پایگاه داده | نشریه الزویر |
نوع نگارش مقاله |
مقاله پژوهشی (Research Article) |
مقاله بیس | این مقاله بیس نمیباشد |
نمایه (index) | Scopus – Master Journals List – JCR |
نوع مقاله | ISI |
فرمت مقاله انگلیسی | |
ایمپکت فاکتور(IF) |
۲٫۶۴۵ در سال ۲۰۱۸ |
شاخص H_index | ۴۴ در سال ۲۰۱۹ |
شاخص SJR | ۰٫۴۷۸ در سال ۲۰۱۸ |
شناسه ISSN | ۱۴۶۷-۰۸۹۵ |
شاخص Quartile (چارک) | Q2 در سال ۲۰۱۸ |
مدل مفهومی | ندارد |
پرسشنامه | ندارد |
متغیر | ندارد |
رفرنس | دارد |
رشته های مرتبط | حسابداری |
گرایش های مرتبط | حسابداری مالی، حسابداری دولتی، حسابرسی |
نوع ارائه مقاله |
ژورنال |
مجله | مجله بین المللی سیستم های اطلاعات حسابداری – International Journal Of Accounting Information Systems |
دانشگاه | Department of Accounting and Information Systems, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan, ROC |
کلمات کلیدی | کشف تقلب، گروه تجاری، صورتهای مالی، متن کاوی |
کلمات کلیدی انگلیسی | Fraud detection، Business group، Financial statement، Texting mining |
شناسه دیجیتال – doi |
https://doi.org/10.1016/j.accinf.2018.11.004 |
کد محصول | E13265 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
فهرست مطالب مقاله: |
Abstract
۱- Introduction ۲- Design of a fraud detection process for financial statements of business groups ۳- Development of fraud detection techniques for financial statements of business groups ۴- Demonstration and evaluation of the proposed approach ۵- Conclusions References |
بخشی از متن 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. 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). |