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Search the complete Maitras.ai archive of policy research papers, analytical notes, and tax essays in one place. Every publication is free to download and released under a Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0) licence, so it can be shared, taught, and built upon with attribution.

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Paper: MCWP-YYYY-NN Author: Sujoy Maitra [ORCID ID: 0009-0009-1319-3706] DOI: [ZENODO ID: XXXXXXXX] Paper URL: www.maitras.ai/research/[paper-slug]

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Policy Insights
August 07, 2026

The Ledger and the Landscape

Testing whether Mali's gold numbers hold up — ranking audit cases by whether production, costs, export prices, royalties and shipments can all be true at once.

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Maitras.ai Research Desk
Policy Insights
August 07, 2026

The Numbers That Can't All Be True

A new test for Ghana's mining revenue — ranking audit cases by whether a company's production, costs, prices, royalties and shipments can all be true at once.

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Maitras.ai Research Desk
Policy Insights
August 07, 2026

Can Physics Catch a Tax Cheat?

What a new algorithm could mean for Burkina Faso's gold revenue — turning physical reality into an additional line of defence for public finance.

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Maitras.ai Research Desk
Policy Insights
August 07, 2026

Enhancing Revenue Transparency for Inclusive Public Finance

How AI-driven tax administration can improve transparency, optimise revenue forecasting, detect fraud, and support evidence-based fiscal policymaking in developing economies.

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Maitras.ai Research Desk
Policy Insights
August 07, 2026

AI in Tax Administration: GovTech and Inclusive Public Finance

Analytical insights for tax data analytics and inclusive economic systems in developing countries — how AI-driven tax administration and GovTech innovation can raise revenue mobilization, reduce fraud, and expand inclusive public finance.

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Maitras.ai Research Desk
Policy Insights
August 07, 2026

Tax Data Analytics for Revenue Optimization

Case study: improving tax forecasting with data analytics in low-income countries — how integrated taxpayer data and predictive models can cut forecast errors from over 15% to under 2%.

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Maitras.ai Research Desk
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