Privacy-preservation Models for Identifying Financial Fraud in Enterprises

doi:10.3969/j.issn.1006-2475.2025.06.003

Abstract

Abstract:
Abstract: To avoid legal and economic barriers to sensitive data and effectively identify financial fraud in enterprises， a machine learning based financial fraud identification model was constructed using the concept of privacy protection. Based on the financial and non-financial information of 19，334 samples from 2012 to 2021， the Internet information was introduced to build a privacy-preservation oriented identification model of enterprise financial fraud （Hetero-SBoost and Hetero-NN）. The results show that the proposed model after the introduction of Internet information， the performance of the optimized models were 7%~10% higher than that of the traditional models， indicating that the introduction of Internet information helps to improve the recognition effect and further optimize the model on the basis of compliance. To further verify the accuracy of that the proposed model in practical applications， a comparison was made between 3，452 samples from 12 companies in Shandong and the results of advanced models （DeepProtect， Starlite）. The results indicate that Hetero-SBoost ensures the overall performance of the model and has better robustness. This paper completes the financial fraud recognition modeling without disclosing data. The introduction of Internet information and privacy protection verifies the effectiveness of the identification model.

Key words: Key words: financial fraud, privacy protection, machine learning, financial information

CLC Number:

TP311.1

XUE Wenxue, XIE Yingde. Privacy-preservation Models for Identifying Financial Fraud in Enterprises[J]. Computer and Modernization, 2025, 0(06): 16-20.

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