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Digital Inclusive Finance and Family Wealth: Evidence from LightGBM Approach

Author

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  • Ying Liu

    (School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China
    Key Laboratory of Financial Technology of Jilin Province, Changchun 130117, China
    Business Big Data Research Center of Jilin Province, Changchun 130117, China)

  • Haoran Zhao

    (School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China)

  • Jieguang Sun

    (Key Laboratory of Financial Technology of Jilin Province, Changchun 130117, China)

  • Yahui Tang

    (Key Laboratory of Financial Technology of Jilin Province, Changchun 130117, China)

Abstract

With the rapid development of digital technology in China, Digital Inclusive Finance, which uses digital financial services to promote financial inclusion, is developing rapidly. This paper uses the Peking University Digital Financial Inclusion index of China and China Family Panel Studies (CFPS) data to construct a predictive model using the LightGBM machine learning algorithm to study whether Digital Inclusive Finance can predict household wealth and analyze the characteristics of strong predictive ability for household wealth. They found that: (1) the introduction of the Digital Financial Inclusion index can improve the prediction performance of the household wealth model; (2) financial literacy and age characteristics are the key characteristics of household wealth accumulation; (3) the coverage and depth of Digital Inclusive Finance has a significant effect on family wealth accumulation, but the degree of digitization acts as a disincentive factor. This paper not only uses machine learning methods to do research on Digital Inclusive Finance and family wealth from a more comprehensive perspective, but also provides effective theoretical support for the key factors that enhance family wealth.

Suggested Citation

  • Ying Liu & Haoran Zhao & Jieguang Sun & Yahui Tang, 2022. "Digital Inclusive Finance and Family Wealth: Evidence from LightGBM Approach," Sustainability, MDPI, vol. 14(22), pages 1-19, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:15363-:d:977173
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    References listed on IDEAS

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    Cited by:

    1. Yu Deng & Wenxia Zhang, 2023. "Modified CPT-TODIM method for evaluating the development level of digital inclusive finance under probabilistic hesitant fuzzy environment," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-22, March.
    2. Yutao Li & Chuanguo Jia & Hong Chen & Hongchen Su & Jiahao Chen & Duoduo Wang, 2023. "Machine Learning Assessment of Damage Grade for Post-Earthquake Buildings: A Three-Stage Approach Directly Handling Categorical Features," Sustainability, MDPI, vol. 15(18), pages 1-23, September.

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