Abstract
Financial accountants falsify financial statements by means of financial techniques such as financial practices and financial standards, and when compared with conventional financial data, it is found that the falsified financial data often lack correlation or even contradict each other in terms of financial data indicators. At the same time, there are also inherent differences in reporting patterns from conventional financial data, but these differences are difficult to test manually. In this paper, the fuzzy C-means (FCM) clustering method is used to amplify these differences and thus identify false financial statements. In the proposed algorithm, firstly, the normalization constraint of the FCM clustering algorithm on the sum of affiliation of individual samples is relaxed to the constraint on the sum of affiliation of all samples, thus reducing the sensitivity of the algorithm to noise and isolated points; secondly, a new affiliation correction method is proposed to address the problem that the difference in affiliation is too large after the relaxation of the constraint. In the discussion of this paper, most of the information comes from the annual reports of companies, administrative penalty decisions of the Securities Regulatory Commission, and some information comes from research reports made by securities institutions, which are limited sources of information. The proposed method can correct the affiliation to a reasonable range, effectively avoiding the problem that some samples have too much affiliation and become a class of their own and also avoiding the problem that it is difficult to choose the termination threshold of the algorithm iteration due to too little affiliation, and can ensure that the constraint on the sum of affiliation of all samples is always satisfied during the iteration of the algorithm. The method has the characteristics of high recognition accuracy and has the significance of theoretical method innovation.
Highlights
Economic globalization is the development trend of today’s world economy, accounting as a business language is moving towards the international process, financial fraud has become a major persistent problem prevailing in countries all over the world, and the manifestations of financial fraud have shown hidden and diversified characteristics [1]. e emergence of these problems, on the one hand, disrupts the normal order of the securities market and affects the healthy development of the securities market; on the other hand, it poses a challenge to the supervision of securities and intermediaries
E recognition of false financial statements based on the fuzzy C-means (FCM) algorithm checks the false financial statements and reduces the incidence of false financial statements. is paper adopts a combination of normative and empirical methods to identify the phenomenon of corporate financial fraud. e empirical study focuses on establishing a logistic regression model and testing it. e research hypothesis establishes a model which can identify financial fraud of companies and test the prediction sample
Is paper is divided into four parts: the first part is the introduction, which introduces the research background and significance of this paper and introduces the research ideas of this paper. e second part is the study of false financial statement identification based on FCM algorithm, reviewing and evaluating the current situation of financial falsification at home and abroad, the determination of financial falsification model samples and indicators, analyzing the problem that the basic FCM algorithm is sensitive to noise and isolated points, analyzing three related algorithms for affiliation correction, and explaining the defects of the algorithms
Summary
Economic globalization is the development trend of today’s world economy, accounting as a business language is moving towards the international process, financial fraud has become a major persistent problem prevailing in countries all over the world, and the manifestations of financial fraud have shown hidden and diversified characteristics [1]. e emergence of these problems, on the one hand, disrupts the normal order of the securities market and affects the healthy development of the securities market; on the other hand, it poses a challenge to the supervision of securities and intermediaries. Rough the in-depth analysis and research on financial fraud, the problems of financial fraud in practice are summarized, which plays a great theoretical significance for future research on the prevention of financial fraud and the forward development of the securities market [4]. E research hypothesis establishes a model which can identify financial fraud of companies and test the prediction sample. E second part is the study of false financial statement identification based on FCM algorithm, reviewing and evaluating the current situation of financial falsification at home and abroad, the determination of financial falsification model samples and indicators, analyzing the problem that the basic FCM algorithm is sensitive to noise and isolated points, analyzing three related algorithms for affiliation correction, and explaining the defects of the algorithms. The model is substituted into the prediction sample for testing, and the model is substituted into other falsifying companies for comparative testing. e fourth part is the conclusion and recommendations. e previous studies are summarized and analyzed, recommendations are made, the main contributions of this paper are noted, and limitations and prospects for future research are pointed out
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
