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Article

Detecting and Analyzing Fraudulent Patterns of Financial Statement for Open Innovation Using Discretization and Association Rule Mining

1
School of Management, Walailak University, Nakhon Si Thammarat 80160, Thailand
2
School of Engineering and Technology, Walailak University, Nakhon Si Thammarat 80160, Thailand
*
Author to whom correspondence should be addressed.
J. Open Innov. Technol. Mark. Complex. 2021, 7(2), 128; https://doi.org/10.3390/joitmc7020128
Received: 5 April 2021 / Revised: 2 May 2021 / Accepted: 6 May 2021 / Published: 9 May 2021
(This article belongs to the Special Issue Financial Open Innovations for Sustainable Economic Growth)
Identifying fraudulent financial statements is important in open innovation to help users analyze financial statements and make investment decisions. It also helps users be aware of the occurrence of fraud in financial statements by considering the associated pattern. This study aimed to find associated fraud patterns in financial ratios from financial statements on the Stock Exchange of Thailand using discretization of the financial ratios and frequent pattern growth (FP-Growth) association rule mining to find associated patterns. We found nine associated patterns in financial ratios related to fraudulent financial statements. This study is different from others that have analyzed the occurrence of fraud by using mathematics for each financial item. Moreover, this study discovered six financial items related to fraud: (1) gross profit, (2) primary business income, (3) ratio of primary business income to total assets, (4) ratio of capitals and reserves to total debt, (5) ratio of long-term debt to total capital and reserves, and (6) ratio of accounts receivable to primary business income. The three other financial items that were different from other studies to be focused on were (1) ratio of gross profit to primary business profit, (2) ratio of long-term debt to total assets, and (3) total assets. View Full-Text
Keywords: detecting fraudulent patterns; financial statement; association rule mining; discretization; innovation detecting fraudulent patterns; financial statement; association rule mining; discretization; innovation
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MDPI and ACS Style

Sawangarreerak, S.; Thanathamathee, P. Detecting and Analyzing Fraudulent Patterns of Financial Statement for Open Innovation Using Discretization and Association Rule Mining. J. Open Innov. Technol. Mark. Complex. 2021, 7, 128. https://doi.org/10.3390/joitmc7020128

AMA Style

Sawangarreerak S, Thanathamathee P. Detecting and Analyzing Fraudulent Patterns of Financial Statement for Open Innovation Using Discretization and Association Rule Mining. Journal of Open Innovation: Technology, Market, and Complexity. 2021; 7(2):128. https://doi.org/10.3390/joitmc7020128

Chicago/Turabian Style

Sawangarreerak, Siriporn, and Putthiporn Thanathamathee. 2021. "Detecting and Analyzing Fraudulent Patterns of Financial Statement for Open Innovation Using Discretization and Association Rule Mining" Journal of Open Innovation: Technology, Market, and Complexity 7, no. 2: 128. https://doi.org/10.3390/joitmc7020128

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