Network Financial Fraud Risk Assessment System Based on Big Data Analysis

Jun, Qi; Yi, Lan

doi:10.1166/jctn.2016.5843

Cited by 3 publications

(1 citation statement)

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“…Since the reform and opening up, China's market economy has gradually become active, the growth rate is increasing day by day, and more and more companies are listed on the stock market [1]. For listed companies, regular financial reports [2] can help managers, investors and creditors to make reasonable judgments. However, for a listed company, it is impossible to operate smoothly.…”

Section: Introductionmentioning

confidence: 99%

Data mining of corporate financial fraud based on neural network model

2020

Computer Optics

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Under the active market economy, more and more listed companies emerge. Because of the various interest relationships faced by listed companies, some enterprises which are not well managed or want to enhance company’s value will choose to forge financial reports by improper means. In order to find out the false financial reports as accurately as possible, this paper briefly introduced the relevant indicators for judging the fraudulence of financial reports of listed companies and the recognition model of financial reports based on back propagation (BP) neural network. Then the selection of the input relevant indexes was improved. The improved BP neural network was simulated and analyzed in MATLAB software and compared with the traditional BP neural network and support vector machine (SVM). The results showed that the importance of total assets net profit, earnings per share, cash reinvestment rate, operating gross profit and pre-tax ratio of profit to debt was the top 5 among 20 judgment indexes. In the identification of testing samples of financial report, the accuracy, precision, recall rate and F value all showed that the performance of the improved BP neural network was better than that of the traditional BP network and SVM.

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Section: Introductionmentioning

confidence: 99%