Enhancing Revenue Transparency for Inclusive Public Finance

Domestic revenue mobilization remains one of the most pressing challenges for developing economies. Many governments struggle with tax evasion, weak compliance systems, fragmented taxpayer databases, and large informal sectors.

According to the OECD Global Revenue Statistics Database, low-income countries average just 13.5% tax-to-GDP — far below the 15% benchmark widely associated with sustainable economic development and effective public service delivery. In Africa, the average tax-to-GDP ratio across 33 countries was 16% in 2022, while the OECD average reached a record high of 34.1% in 2024.

At the same time, governments face mounting expectations to finance healthcare, education, climate adaptation, and social protection. Traditional tax administration methods are insufficient for these growing fiscal responsibilities.

AI in tax administration offers a transformative opportunity. By combining machine learning, predictive analytics, and large-scale taxpayer data integration, governments can strengthen compliance monitoring, reduce fraud, improve audit selection, and generate more accurate revenue forecasts.

Organizations such as the OECD, IMF, and UNDP increasingly emphasize the role of digital public finance systems in achieving Sustainable Development Goals (SDGs). See: OECD Revenue Statistics 2025.

Modern tax data analytics systems integrate multiple datasets including taxpayer registration databases, customs and trade records, GST/VAT filings, mobile payment ecosystems, banking transactions, payroll records, and corporate financial disclosures.

AI models are applied across three major operational areas:

AI-driven tax administration workflow

1. Data collection
2. Taxpayer profiling
3. AI risk scoring
4. Fraud detection
5. Revenue forecasting
6. Policy decision support

Risk-based taxpayer segmentation

Machine learning algorithms classify taxpayers by compliance risk. High-risk entities are prioritized for audits, reducing administrative costs and improving enforcement efficiency. The IRS now deploys AI-powered issue recommenders in both its Large Business & International and Small Business/Self-Employed divisions.

Revenue forecasting models

Predictive analytics models use historical tax collection trends, macroeconomic indicators, and sectoral growth patterns to estimate future revenue performance — enabling better fiscal planning and budget credibility.

Fraud and anomaly detection

AI systems identify unusual filing patterns, suspicious refund claims, transfer pricing irregularities, shell company networks, and invoice mismatches. Research published by the International Growth Centre shows that AI-powered customs algorithms raised fraud detection rates from 18% to 73% among inspected shipments, while cutting full inspection volumes by around 70%.

AI adoption in tax administration

The 2024 OECD Inventory of Tax Technology Initiatives (ITTI) found that 29 of 38 OECD members now use AI in tax administration. Primary application areas: detection of tax evasion and fraud, decision-making assistance, and improving taxpayer services.

Fraud detection results

The US Treasury's AI-driven fraud detection initiative prevented and recovered over USD 4 billion in improper payments in fiscal year 2024 — a six-fold increase from USD 652.7 million the prior year. IRS Criminal Investigation identified USD 9.1 billion in tax fraud in FY2024, up from USD 2.3 billion in FY2020, as investigations scaled from 1,598 to 2,667 cases.

TIWB: international collaboration results

The joint OECD-UNDP Tax Inspectors Without Borders initiative has, over ten years, helped developing countries collect over USD 2.4 billion in additional tax revenues and generate USD 6.05 billion in additional tax assessments across 62 jurisdictions. African countries are particularly strong beneficiaries, with TIWB and ATAF together supporting over USD 1.8 billion in additional collections. According to UN estimates, every dollar invested in TIWB programs generates over USD 100 in increased domestic revenues.