Detecting the financial statement fraud: The analysis of the differences between data mining techniques and judgments
The objective of this study is to identify and ranking of factors affecting detecting the financial statement frauds using the judgment technique based on the Analytic Hierarchy Process and data mining techniques techniques. The population of the study comprised of senior auditors, supervisors, seni...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | fas |
| Published: |
Securities Exchange
2020-11-01
|
| Series: | فصلنامه بورس اوراق بهادار |
| Subjects: | |
| Online Access: | https://journal.seo.ir/article_11190_c777a450511809fbbd74a2e9fc3446f4.pdf |
| _version_ | 1818205078239051776 |
|---|---|
| author | javad masoumi Hashem Nikoomaram Ghodrat Allah Talebnia Fraydoon Rahnamay Roodposhti |
| author_facet | javad masoumi Hashem Nikoomaram Ghodrat Allah Talebnia Fraydoon Rahnamay Roodposhti |
| author_sort | javad masoumi |
| collection | DOAJ |
| description | The objective of this study is to identify and ranking of factors affecting detecting the financial statement frauds using the judgment technique based on the Analytic Hierarchy Process and data mining techniques techniques. The population of the study comprised of senior auditors, supervisors, senior supervisors, audit manager and partner of the audit institute employed in audit institutes member and also companies listed in the Tehran Stock Exchange. In order to the research goal, 56 questionnaires and 109 Listed for the year 2012-2017 and analyzed. Based on the technique of judgment, the pressure dimension of the first priority, opportunity, second factor and rationalization are ranked as the third effective factor on the detection of fraud. These results are different with other techniques. Empirically, the ANNs and CART approaches work with the training and testing samples in a correct classification rate of 98/65% (ANNs) & 91.5% (CART) and 69/79% (ANNs) & 69.10% (CART), respectively, which is more accurate than the logistic model that only reaches 72.32% and 88.10% of the correct classification in assessing the fraud presence. In addition, type II error of CART drops significantly to 58.18% from 72.7% and 55.6% compared to the ones using ANNs and logistic models. According to the accuracy index, the decision tree model is more efficient than other models; therefore, among the data mining techniques, the weight of each of the input variables of the decision tree is the basis for the final ranking of the research variables |
| first_indexed | 2024-12-12T03:51:25Z |
| format | Article |
| id | doaj.art-53ec19af2d0d44df8773c5da5d93e10e |
| institution | Directory Open Access Journal |
| issn | 2228-5431 2820-9893 |
| language | fas |
| last_indexed | 2024-12-12T03:51:25Z |
| publishDate | 2020-11-01 |
| publisher | Securities Exchange |
| record_format | Article |
| series | فصلنامه بورس اوراق بهادار |
| spelling | doaj.art-53ec19af2d0d44df8773c5da5d93e10e2022-12-22T00:39:21ZfasSecurities Exchangeفصلنامه بورس اوراق بهادار2228-54312820-98932020-11-01135111914010.22034/jse.2020.1119011190Detecting the financial statement fraud: The analysis of the differences between data mining techniques and judgmentsjavad masoumi0Hashem Nikoomaram1Ghodrat Allah Talebnia2Fraydoon Rahnamay Roodposhti3PhD Candidate in Accounting, Science and Research Branch, Islamic Azad University, Tehran, IranProfessor, Department of Accounting, Science and Research Branch, Islamic Azad University, Tehran, IranAssociate Prof. Department of Accounting, Science and Research Branch, Islamic Azad University, Tehran, IranProfessor, Department of Accounting, Science and Research Branch, Islamic Azad University, Tehran, IranThe objective of this study is to identify and ranking of factors affecting detecting the financial statement frauds using the judgment technique based on the Analytic Hierarchy Process and data mining techniques techniques. The population of the study comprised of senior auditors, supervisors, senior supervisors, audit manager and partner of the audit institute employed in audit institutes member and also companies listed in the Tehran Stock Exchange. In order to the research goal, 56 questionnaires and 109 Listed for the year 2012-2017 and analyzed. Based on the technique of judgment, the pressure dimension of the first priority, opportunity, second factor and rationalization are ranked as the third effective factor on the detection of fraud. These results are different with other techniques. Empirically, the ANNs and CART approaches work with the training and testing samples in a correct classification rate of 98/65% (ANNs) & 91.5% (CART) and 69/79% (ANNs) & 69.10% (CART), respectively, which is more accurate than the logistic model that only reaches 72.32% and 88.10% of the correct classification in assessing the fraud presence. In addition, type II error of CART drops significantly to 58.18% from 72.7% and 55.6% compared to the ones using ANNs and logistic models. According to the accuracy index, the decision tree model is more efficient than other models; therefore, among the data mining techniques, the weight of each of the input variables of the decision tree is the basis for the final ranking of the research variableshttps://journal.seo.ir/article_11190_c777a450511809fbbd74a2e9fc3446f4.pdffraud in financial statementsauditor judgmentartificial neural networksdecision treesanalytic hierarchy process method |
| spellingShingle | javad masoumi Hashem Nikoomaram Ghodrat Allah Talebnia Fraydoon Rahnamay Roodposhti Detecting the financial statement fraud: The analysis of the differences between data mining techniques and judgments فصلنامه بورس اوراق بهادار fraud in financial statements auditor judgment artificial neural networks decision trees analytic hierarchy process method |
| title | Detecting the financial statement fraud: The analysis of
the differences between data mining techniques and judgments |
| title_full | Detecting the financial statement fraud: The analysis of
the differences between data mining techniques and judgments |
| title_fullStr | Detecting the financial statement fraud: The analysis of
the differences between data mining techniques and judgments |
| title_full_unstemmed | Detecting the financial statement fraud: The analysis of
the differences between data mining techniques and judgments |
| title_short | Detecting the financial statement fraud: The analysis of
the differences between data mining techniques and judgments |
| title_sort | detecting the financial statement fraud the analysis of the differences between data mining techniques and judgments |
| topic | fraud in financial statements auditor judgment artificial neural networks decision trees analytic hierarchy process method |
| url | https://journal.seo.ir/article_11190_c777a450511809fbbd74a2e9fc3446f4.pdf |
| work_keys_str_mv | AT javadmasoumi detectingthefinancialstatementfraudtheanalysisofthedifferencesbetweendataminingtechniquesandjudgments AT hashemnikoomaram detectingthefinancialstatementfraudtheanalysisofthedifferencesbetweendataminingtechniquesandjudgments AT ghodratallahtalebnia detectingthefinancialstatementfraudtheanalysisofthedifferencesbetweendataminingtechniquesandjudgments AT fraydoonrahnamayroodposhti detectingthefinancialstatementfraudtheanalysisofthedifferencesbetweendataminingtechniquesandjudgments |