Paper
The Impact of Corporate AI Washing on Farmers' Digital Financial Behavior Response -- An Analysis from the Perspective of Digital Financial Exclusion
Authors
Li Wenxiu, Wen Zhanjie, Xia Jiechang, Guo Jingqiao
Abstract
In the context of the rapid development of digital finance, some financial technology companies exhibit the phenomenon of "AI washing," where they overstate their AI capabilities while underinvesting in actual AI resources. This paper constructs a corporate-level AI washing index based on CHFS2019 data and AI investment data from 15-20 financial technology companies, analyzing and testing its impact on farmers' digital financial behavior response. The study finds that AI washing significantly suppresses farmers' digital financial behavior; the higher the degree of AI washing, the lower the response level of farmers' digital financial behavior. Moreover, AI washing indirectly inhibits farmers' behavioral responses by exacerbating knowledge exclusion and risk exclusion. Social capital can positively moderate the negative impact of AI washing; among farmer groups with high social capital, the suppressive effect of AI washing on digital financial behavior is significantly weaker than that among groups with low social capital. In response, this paper suggests that regulatory authorities establish a strict information disclosure system for AI technology, conduct differentiated digital financial education to enhance the identification capabilities of vulnerable groups, promote digital financial mutual aid groups to leverage the protective effects of social capital, improve the consumer protection mechanism for farmers in digital finance, and set up pilot "Digital Inclusive Finance Demonstration Counties," etc.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.18421v1</id>\n <title>The Impact of Corporate AI Washing on Farmers' Digital Financial Behavior Response -- An Analysis from the Perspective of Digital Financial Exclusion</title>\n <updated>2026-03-19T02:29:36Z</updated>\n <link href='https://arxiv.org/abs/2603.18421v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.18421v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>In the context of the rapid development of digital finance, some financial technology companies exhibit the phenomenon of \"AI washing,\" where they overstate their AI capabilities while underinvesting in actual AI resources. This paper constructs a corporate-level AI washing index based on CHFS2019 data and AI investment data from 15-20 financial technology companies, analyzing and testing its impact on farmers' digital financial behavior response. The study finds that AI washing significantly suppresses farmers' digital financial behavior; the higher the degree of AI washing, the lower the response level of farmers' digital financial behavior. Moreover, AI washing indirectly inhibits farmers' behavioral responses by exacerbating knowledge exclusion and risk exclusion. Social capital can positively moderate the negative impact of AI washing; among farmer groups with high social capital, the suppressive effect of AI washing on digital financial behavior is significantly weaker than that among groups with low social capital. In response, this paper suggests that regulatory authorities establish a strict information disclosure system for AI technology, conduct differentiated digital financial education to enhance the identification capabilities of vulnerable groups, promote digital financial mutual aid groups to leverage the protective effects of social capital, improve the consumer protection mechanism for farmers in digital finance, and set up pilot \"Digital Inclusive Finance Demonstration Counties,\" etc.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CY'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.AI'/>\n <published>2026-03-19T02:29:36Z</published>\n <arxiv:comment>Comments: 35 pages, 4 tables, empirical research on rural digital finance & fintech, using CHFS2019 data (6,800 rural households) & corporate AI investment data, incorporating Logit/Ologit/GSEM models, suitable for agricultural economics/financial inclusion journals</arxiv:comment>\n <arxiv:primary_category term='cs.CY'/>\n <author>\n <name>Li Wenxiu</name>\n </author>\n <author>\n <name>Wen Zhanjie</name>\n </author>\n <author>\n <name>Xia Jiechang</name>\n </author>\n <author>\n <name>Guo Jingqiao</name>\n </author>\n </entry>"
}