Fraud detection for financial statements of business groups

Autor: Wan Ching Liou, Yuh-Jen Chen, Yuh-Min Chen, Jyun Han Wu
Rok vydání: 2019
Předmět:
Zdroj: International Journal of Accounting Information Systems. 32:1-23
ISSN: 1467-0895
DOI: 10.1016/j.accinf.2018.11.004
Popis: 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.
Databáze: OpenAIRE
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