An article in Tax Research discusses how to strengthen the foundations of tax and fee collection during the current wave of digital transformation. Its point is straightforward: data has become the tax authority's most important asset, and smart tools can reshape collection workflows, yet the old approach clearly lags in data sharing, joint governance and day-to-day supervision, so institutions and technology must be fixed together. It makes the case along four lines. The first is digital government thinking, whose core is not to bolt technology on but to embed it in institutions, consolidating collection, service and audit onto one platform, tearing down data walls between departments and adding cross-agency sharing and intelligent risk warning. The second is algorithmic governance: algorithms are no longer passive tools, the article argues, and can help make judgements, spot risks and allocate resources, for instance through machine-learning risk models, abnormal behaviour monitoring and taxpayer credit scoring, and by pushing tax policy precisely to the right firms by industry and size. The third is organisational change, covering structure, capability and culture at once. The paper also flags weak spots in data sharing, keeping daily supervision up to speed and deploying talent, and closes with a package of operational recommendations aimed at shoring up the foundations and lifting governance performance.
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Rethinking How to Strengthen the Foundations of Tax Collection Amid Digital Transformation
An article in the journal Tax Research examines how to shore up the foundations of tax collection under digital transformation. It sets out four analytical dimensions. Digital government theory stresses embedding technology in institutions, integrating collection, service and audit onto a single platform, and using big data to break down departmental silos, build cross-agency sharing and create intelligent risk warning systems. Algorithmic governance theory argues that algorithms have shifted from passive tools to active participants in governance, supporting machine-learning-based risk identification, abnormal behaviour monitoring and taxpayer credit scoring, as well as matching and pushing policy to firms by industry and size. Organisational change theory points to a combined transformation of structure, capability and governance. The paper also reviews shortcomings in data sharing, co-governance, day-to-day supervision and talent support, and offers operational recommendations.
2026-10-10