Abstract
AbstractThis article selects 68 financial report fraud companies that received administrative penalties from the Securities Regulatory Commission for financial report fraud from 2010 to 2019 as the research sample, and correspondingly selects 68 non-fraud companies as the corresponding samples. The K-nearest neighbor algorithm was used to analyze the text of the annual report of the sample company. The research found that the text in the annual report could effectively reflect the fraud behavior of the listed company, and the text analysis could effectively identify the fraud behavior of the financial report. In addition, comparing the two types of samples, it is found that non-fraud companies use more diverse words in their annual reports, and it is more difficult to extract typical text features. This leads to the recognition results of fraud companies in text recognition models being superior to non-fraud companies.KeywordsFinancial report fraud identificationText analysisK-nearest neighbor algorithm
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