AI Could Help Arab States Boost Tax Collection, But Governance Is Key, Arab Monetary Fund Says

(MENAFN- Khaleej Times) Artificial intelligence could help Arab governments improve tax collection and make public finances more efficient, but technology alone will not guarantee higher revenues without strong institutions, governance and digital infrastructure, according to findings presented by the Arab Monetary Fund in Abu Dhabi.

The findings were presented during the 17th Ordinary Session of the Council of Arab Finance Ministers, which brought together finance ministers and senior officials to discuss fiscal policy, public finances and the use of artificial intelligence in tax administration. Pasted markdown

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AI readiness linked to stronger tax collection

An AMF study presented during the meeting examined 114 countries, including 15 Arab economies, between 2020 and 2024, using 570 observations to assess the relationship between governments' AI readiness and their ability to mobilize tax revenues.

The analysis found a positive relationship between AI readiness and tax collection, with the estimated effect reaching 0.42 under traditional econometric models and 0.53 under machine-learning models.

However, the Fund cautioned that greater AI readiness does not automatically translate into higher tax revenues.

Governance quality emerged as the strongest determinant of tax collection in the machine-learning analysis, alongside regulatory quality, rule of law, trade openness, GDP per capita and AI readiness.

The Fund said AI can be an important tool for improving tax collection but works more effectively when it forms part of an integrated institutional and governance framework.

Impact differs across Arab economies

An AMF simulation examining a 10-point improvement in AI readiness found an average estimated tax-revenue impact of 0.69, but the results differed significantly according to economic structure and tax systems.

The estimated impact reached 4.63 for oil economies with relatively low taxation, compared with 0.21 for oil-dependent economies, while oil economies with higher taxation recorded a negative 0.7 reading.

The findings indicate that there is no single approach to AI adoption, with results depending on each country's economic structure, tax system and institutional environment.

UAE outlines digital transformation experience

Mohamed bin Hadi Al Hussaini, UAE Minister of State for Financial Affairs, said successful AI adoption requires more than investment in technology.

He said the UAE's digital transformation began by moving away from paper-based transactions, followed by shifting government services to mobile platforms and developing digital identity systems.

These steps created the infrastructure needed for more advanced applications of AI, he said.

The UAE is looking at expanding the role of AI assistants across government services, moving beyond data analysis towards answering questions, accessing information and assisting users.

Al Hussaini also stressed the importance of governance and cybersecurity as governments become increasingly dependent on digital systems. Pasted markdown

UAE, Saudi Arabia among regional adopters

The AMF presentation highlighted the UAE and Saudi Arabia for the use of advanced tax tools including risk-analysis models, compliance controls, electronic invoicing and data analytics.

Bahrain was highlighted for broader government digital transformation and capacity building, while Egypt has started pilot AI projects within its tax administration.

Morocco has advanced tax digitalization, including electronic invoicing, data analysis and the selection of audit cases using digital tools.

AI already used by 70% of tax administrations

Around 70 per cent of tax administrations are using AI, particularly for fraud detection, risk assessment and digital interaction with taxpayers, according to figures presented by the AMF.

The Fund said tax authorities are turning to AI as tax systems become more complex, financial transactions increasingly move online and administrations face pressure on human resources.

Some tax administrations have an average of around one employee for every 1,000 taxpayers.

Global investment in AI applications for tax administrations is expected to reach approximately $2.3 billion by 2027. Pasted markdown

US generated $1.3 billion in additional collections

International examples presented by the AMF showed how tax authorities are already applying the technology.

The United States generated more than $1.3 billion in additional collections using risk-classification models, while Russia recorded €354 million in additional revenues in 2024.

Italy was cited as identifying more than one million high-risk cases annually, while France has used satellite imagery to detect undeclared property developments.

Brazil has used digital tools to improve VAT collection, reduce errors in tax returns and speed up refunds, while Singapore uses a virtual tax assistant to respond to taxpayers.

Skills shortage, costs pose challenges

More than 70 per cent of AI projects face shortages of specialized skills, according to figures presented by the AMF.

Implementation costs for a medium-sized country can range between $5 million and $50 million, while around 60 per cent of projects encounter difficulties integrating AI with existing systems.

Other challenges include computing and data-storage requirements, cybersecurity, privacy and data protection, legal accountability and employee resistance to change.

Arab governments spend 29.4% of GDP

The discussions also examined broader pressures on Arab public finances.

Average government expenditure across Arab countries stands at 29.4 per cent of GDP, rising to 31.8 per cent in emerging Arab economies, according to figures presented by the AMF.

Current expenditure accounts for around 79 per cent of total government spending.

The Fund identified rigid budget structures, weak investment spending and limited fiscal space among the major challenges.

In some Arab countries, public-sector wage bills exceed 20 per cent of GDP and account for more than 50 per cent of current expenditure.

Subsidies can account for as much as 56 per cent of current expenditure in some countries, while energy subsidies across Arab economies average around 7 per cent of GDP, compared with a global average of about 0.9 per cent.

Interest payments can consume more than one-third of current expenditure in some Arab countries and exceed 60 per cent in certain cases.

The AMF highlighted reforms including better targeting of subsidy beneficiaries, gradual price adjustments, direct cash transfers, domestic debt-market development and medium-term debt-management strategies.

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