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The analysis of influence mechanism for internet financial fraud identification and user behavior based on machine learning approaches

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Abstract

The study is to explore the risks in the Internet finance and the factors affecting users' behavior under the background of big data. First, the risks of the Internet finance under the background of big data and the existing risk control modes are analyzed. Then, based on BP neural network (BPNN), an Internet financial fraud identification model is constructed, and corresponding touch rules are made. Its prediction performance is quantitatively compared with that of support vector machine and random forest algorithm. Finally, based on the structural equation model, the influence path of perceived security control on the Internet financial behavior is explored. The results show that, the applicants whose unit addresses are on blacklist have the highest touch fraud rate (14.16%). The precision rate (88.14%), accuracy rate (96.37%), recall rate (70.96%), and F-Score value (16.36) of the financial fraud identification model based on BPNN are the highest versus the other two algorithms, and the error detection rate (7.19%) is the lowest. The perceived security, identity authentication, non-repudiation of transactions, privacy protection, and control strength of data integrity positively affect users’ trust, which further positively affects the attitude and intention of using the Internet finance, and the intention eventually affects users’ behavior. Finally, some suggestions are put forward to improve the supervision of the Internet finance in China. To sum up, the Internet financial fraud identification model based on BPNN demonstrates satisfying performance and is worth of promotion. Additionally, the authentication technology, non-repudiation of transactions, privacy protection, data integrity, and users' sense of trust of the Internet finance have a significant impact on users' behavior.

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Authors and Affiliations

  1. College of Economics, Sichuan Agricultural University, Chengdu, 611130, China

    Tianlang Xiong

  2. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China

    Zhishuo Ma

  3. College of Communication Engineering, Jilin University, Changchun, 130012, China

    Zhuangzhuang Li & Jiangqianyi Dai

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  1. Tianlang Xiong
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  2. Zhishuo Ma
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  3. Zhuangzhuang Li
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  4. Jiangqianyi Dai
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Correspondence to Tianlang Xiong.

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Xiong, T., Ma, Z., Li, Z. et al. The analysis of influence mechanism for internet financial fraud identification and user behavior based on machine learning approaches. Int J Syst Assur Eng Manag 13 (Suppl 3), 996–1007 (2022). https://doi.org/10.1007/s13198-021-01181-0

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  • DOI: https://doi.org/10.1007/s13198-021-01181-0

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