The objective of this study is to apply an unsupervised neural network tool to analyze fraudulent financial reporting (FFR) by extracting distinguishing features from samples of groups of companies and converting them into useful information for FFR detection. This methodology can be used as a decision support tool to help build an FFR identification model or other financial fraud or financial ...

62 Abstract— With an increase in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection has become an emerging topics of great importance for academics, research and industries. Financial fraud is a deliberate act that is contrary to law, rule or policy with intent to obtain unauthorized financial benefit and intentional misstatements or om...

Exploring Financial Reporting Fraud with GHSOM Rua-Huan Tsaih, Wan-Ying Lin, Shin-Ying Huang Identifying Firm-Specific Risk Statements in News Articles Hsin-Min Lu, Wan-Hsin Huang, Zhu Zhang, Tsai-Jyh Chen Predicting Future Earnings Change Using Numeric and Textual Information in Financial Reports * Kuo-Tay Chen, JuChun Yen, Tsai-Jyh Chen 11: 35 AM Session 3: Emergency Response and Surveillance...

Empirical studies of earnings management, audit pricing, and audit reporting provide extensive evidence that the Big Five public accounting firms are associated with higher quality financial statements (Francis 2004). Nevertheless, the recent high-profile financial reporting failures that roiled the U.S. capital markets cast doubt on whether this proposition remains valid. Indeed, many commenta...

Enterprise systems, real time recording and real time reporting pose new and significant challenges to the accounting and auditing professions. This includes developing methods and tools for continuous assurance and fraud detection. In this paper we propose a methodology for continuous fraud detection that exploits security audit logs, changes in master records and accounting audit trails in en...