The article looks at how large language models can make tax compliance sturdier for platform companies. Such firms face two persistent problems: tax rules change quickly, and tax-related data is scattered and messy, so the tax base often does not add up. The authors propose two foundations first. One is a dynamic tax-law matching engine that swaps in new rules as regulations change. The other is a panoramic tax-data base that strings scattered data into a single line. On top of these sits a cluster of intelligent applications that automate filing, risk control and services separately. The article cautions that models are not free to run: they disrupt how tax obligations are met and how legal liability is assigned. It therefore proposes three lines of defense: full-scope security protection for tax-related data, three-dimensional constraints on model-driven decisions, and humans and machines jointly guarding compliance.
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Large Language Models for Platform-Enterprise Tax Compliance: Challenges, Mechanisms and Risk Governance
An article in the October 2026 issue of Taxation Research examines how large language models can support tax compliance at platform companies. It argues that fast-changing tax rules and fragmented, heterogeneous tax-related data undermine accurate application of tax law and the completeness of the tax base. The proposed remedy is a two-layer foundation: a dynamic tax-law matching engine that keeps rules synchronized in real time, and a panoramic tax-data base that makes the tax base consistent, on top of which an intelligent tax application cluster drives automated compliance, smarter risk control and personalized service. The authors also warn that deployment disrupts how tax obligations and legal liability are defined, and call for data-security safeguards, three-dimensional constraints on model-driven decisions and human-machine collaborative compliance defense.
2026-11-04