[Retracted] Using an Optimized Learning Vector Quantization‐ (LVQ‐) Based Neural Network in Accounting Fraud Recognition

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.

Financial Fraud Identification Based on Stacking Ensemble Learning Algorithm: Introducing MD&A Text Information

“…Some scholars have found that abnormal financial indicator data can provide direct clues for the identification of financial fraud [ 46 , 47 ]. In this regard, in accordance with the principles of scientific, systematic, and comprehensive variable selection, this study refers to relevant research on the identification of financial fraud by Zheng et al [ 48 ], selecting financial indicators from eight aspects: solvency, operating capacity, profitability development ability, per share index, cash flow index, relative value index, and risk level. Among these, solvency reflects the ability of an enterprise to repay its debts.…”

Retracted: Using an Optimized Learning Vector Quantization‐ (LVQ‐) Based Neural Network in Accounting Fraud Recognition

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”