Abstract
With the continuous development and wide application of artificial intelligence technology, artificial neural network technology has begun to be used in the field of fraud identification. Among them, learning vector quantization (LVQ) neural network is the most widely used in the field of fraud identification, and the fraud identification rate is relatively high. In this context, this paper explores this neural network technology in depth, uses the same fraud sample to test the fraud recognition rate of these two models, and proposes an optimized LVQ-based combined neural network fraud risk recognition model on this basis. This paper selects 550 listed companies that have committed fraud from 2015 to 2019 as the fraud samples, determines 550 nonfraud matching sample companies in accordance with the Beasley principle one-to-one, and uses this as the research sample. The fraud risk identification indicators with better identification effects combed out according to the literature were used as the initial indicator system. After the collinearity problem was eliminated through the paired sample T test and principal component analysis, the five indicators with the best identification effects were finally selected. Finally, based on the above theoretical analysis and empirical research summarizing the full text, it analyzes the shortcomings of this research and puts forward prospects for the future development of fraud risk identification models.
Highlights
Fraud has severely affected the public’s confidence in the accounting community and the capital market; how to effectively identify corporate fraud has become the top priority of accounting theory, practice, and regulatory agencies. e empirical research shows that the model fraud identification effect is better than the fraud case analysis, and the construction of an effective fraud risk identification model is inseparable from perfect fraud identification indicators and appropriate identification methods
This paper explores this neural network technology in depth, uses the same fraud sample to test the fraud recognition rate of these two models, and proposes an optimized learning vector quantization (LVQ)-based combined neural network fraud risk recognition model on this basis. is paper selects listed companies that have committed fraud from 2015 to 2019 as the fraud samples, uses the fraud risk identification indicators with better identification results combed out according to the literature as the initial indicator system, and eliminates the commonality through paired sample T test and principal component analysis
In the process of network learning, the assignment category of input samples is specified through the tutor signal, thereby overcoming the lack of classification information caused by the use of unsupervised learning algorithms in self-organizing networks weakness. e biggest advantage of LVQ neural network is that it cannot only classify linear input data, and process multidimensional data containing noise interference
Summary
Fraud has severely affected the public’s confidence in the accounting community and the capital market; how to effectively identify corporate fraud has become the top priority of accounting theory, practice, and regulatory agencies. e empirical research shows that the model fraud identification effect is better than the fraud case analysis, and the construction of an effective fraud risk identification model is inseparable from perfect fraud identification indicators and appropriate identification methods. According to the definition of environment and corporate environment, this article believes that the environment of accounting fraud refers to a collection of interrelated, mutually restrictive, and constantly changing factors that affect the existence of accounting fraud, including internal environmental characteristics and external environmental characteristics. E internal environmental characteristics of accounting fraud refer to the collection of various factors that exist within the enterprise and affect accounting fraud [6] It includes corporate governance structure, institutional settings, the implementation of the system, and so on. E external environmental characteristics of accounting fraud refer to the collection of various factors that exist outside the enterprise and affect accounting fraud It includes external corporate governance, market environment, political environment, and economic environment. Based on the above theoretical analysis and empirical research summarizing the full text, it analyzes the deficiencies in this research and puts forward a prospect for the future development of the fraud risk identification model
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
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 call
