Title: Early warning of financial statement data leakage risk based on grey fuzzy comprehensive evaluation

Authors: Shanshan Li; Dannan Lin

Addresses: School of Finance and Accounting, Fujian Business University, Fujian Fuzhou, China ' School of Information Engineering, Fujian Business University, Fujian Fuzhou, China

Abstract: In order to improve the low accuracy of risk identification and early warning success rate of traditional data leakage risk early warning methods, this paper proposes a financial statement data leakage risk early warning method based on grey fuzzy comprehensive evaluation. Firstly, the grey fuzzy matrix and the set of risk identification factors are constructed. Secondly, according to the factor set constructed above, the weight of each factor is calculated, and the risk of data leakage of financial statements by using grey fuzzy comprehensive evaluation method is accurately identified. Finally, the least squares support vector mechanism is used to build the financial data report data leakage risk early warning function; the output result of the early warning function is the early warning result. The experimental results show that the risk identification accuracy of this method is high, and the average success rate of early warning is 99.07%.

Keywords: grey fuzzy comprehensive evaluation; financial statement data; leakage risk warning.

DOI: 10.1504/IJASS.2023.134366

International Journal of Applied Systemic Studies, 2023 Vol.10 No.3, pp.232 - 242

Received: 23 Dec 2021
Accepted: 09 Jul 2022
Published online: 19 Oct 2023 *

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