Abstract
In modern market economy, corporate financial frauds emerge one after another, which have a huge impact on the stock market and triggered an unprecedented credit crisis. Therefore, it is particularly important to identify financial frauds. Improving the efficiency, accuracy, and coverage of fraud identification in financial reports through digital and intelligent means is one of the important links to improve credit risk control of securities companies and also for securities companies to accurately price related financial products of target companies. Traditional recognition methods based on artificial rules can cover relatively limited indicators; rule parameters are set randomly. Besides, it is difficult to make rules based on high-dimension indicators and to dig the hidden deep relationships between indicators. This paper summarizes the relevant indicators of financial fraud identification from the perspective of financial and nonfinancial characteristics. Then, the identification and early warning of financial fraud risk based on bidirectional long-short term memory model are proposed. This method uses the idea of ensemble learning, weights the probability of financial key indicators, and uses the optimal transfer probability to solve the financial fraud risk results. The results show that the industry-specific modeling can significantly improve the accuracy of the financial fraud identification model and it also can effectively help the government regulatory departments, investors, and audit departments to correctly identify the financial fraud of listed companies.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.