From potential to policy: How AI can reshape public finance

Gulf Business

15 hours ago

Image: WAM/ For illustrative purposes
Governments worldwide face mounting fiscal pressure: rising debt, volatile revenues, and growing public expectations.
Amid this complexity, they are expected to act faster, spend smarter, and enhance trust in public institutions. Artificial intelligence (AI) presents a once-in-a-generation opportunity to redefine how public resources are planned, allocated, and accounted for. Yet its potential in public finance remains largely untapped.
Governments are beginning to integrate AI into fiscal operations – from optimising budgets and improving forecasts to automating audits and fraud detection. These early efforts hint at a bigger prize: the strategic use of AI to redesign the fiscal policy cycle itself.
The question is no longer whether AI can help, but how fast and how well governments can scale its use responsibly.
AI's role in modern public finance
AI is transforming how governments manage public finances. It enhances decision-making in fiscal policy and resource allocation, strengthens risk management, streamlines operations, and improves citizen-facing services.
By moving beyond simple automation, AI enables real-time data analysis, dynamic resource targeting, and proactive risk identification.
These capabilities are already being applied across public finance to support:
Macroeconomic and fiscal forecasting: AI is transforming traditional econometric methods by using machine learning (ML) and deep learning (DL) to process vast, unstructured datasets. This improves forecasting accuracy and enables real-time 'nowcasting'. For instance, the Australian Taxation Office uses ML models to forecast tax revenues, while South Korea's Ministry of Economy and Finance produces daily updates on the national treasury balance using AI. In the UAE, the Ministry of Finance is enhancing revenue forecasting and compliance through AI, while initiatives like Smart Dubai embed intelligent tools in services such as digital payments and smart procurement.
Budget planning and expenditure monitoring: AI modernises budgeting processes by automating data handling and applying advanced analytics. ML enhances the accuracy of expenditure baselines, supports policy cost estimation, and enables evidence-based fiscal decision-making. For example, the Australian Department of Veterans' Affairs uses predictive models to simulate lifetime fiscal impacts of beneficiaries and assess policy options, while France's DGFiP applies ML to identify municipalities at financial risk, evolving from historical data analysis to predictive forecasting.
Public spending reviews: AI is strengthening spending review processes by analyzing large and complex datasets to identify trends, evaluate programme effectiveness, and inform resource reallocation. ML and DL extend beyond traditional analytics to uncover deeper insights and automate recommendations. For instance, the UK Treasury employs HMT-GPT to assess budget proposals and support long-term funding reviews, while Canada's Department of Finance uses AI to evaluate the impact of public spending and guide reallocation decisions.
Accounting, control, and fraud detection: AI-driven automation and anomaly detection are making internal financial controls more efficient. Tools using NLP, ML, and DL, can rapidly process documents, identify irregularities, and strengthen oversight. Denmark employs AI to monitor subsidy disbursements and flag anomalies, and the UK applies ML to detect fraudulent benefit claims with improved speed and accuracy.
Citizen engagement and service delivery: AI is redefining how public finance institutions interact with citizens. Chatbots and language models enhance accessibility, automate responses, and improve transparency. The U.S. Internal Revenue Service uses AI-powered voice and chatbots to reduce inquiry wait times, while Ireland's Department of Finance uses AI to draft tax manuals and summarize legal documents, making government communication more accessible.
These examples underscore AI's growing role across the fiscal value chain, but also reveal a gap: AI is informing decisions, not making them.
Why prescriptive AI remains elusive
Prescriptive AI – the ability to recommend or make decisions – remains rare in public finance.
The reasons are complex: lack of explainability, unclear accountability, and unresolved ethical concerns. Should an AI system decide how public funds are distributed or which programs face cuts? What if its recommendations reflect bias or flawed assumptions? Who is accountable when things go wrong? These are not just technical questions – they are governance questions. Addressing them is key to unlocking AI's next frontier in fiscal policymaking.
What's holding AI back?
Despite its promise, AI adoption in public finance faces five persistent barriers:
Lack of strategic alignment with institutional priorities: Many institutions lack a top-down, structured approach to identifying AI use cases that directly support national priorities or institutional mandates. This leads to fragmented, opportunistic, or siloed implementations and limits the ability to demonstrate strategic value, especially when impact tracking is focused solely on cost or operational efficiency.
Outdated infrastructure and fragmented data ecosystems: Legacy IT systems and non-integrated data sources hinder effective AI model development. High-quality, interoperable data is essential but often inaccessible or trapped in bureaucratic systems resistant to integration. These challenges are particularly acute in regions where coordination across agencies remains limited. In the GCC, efforts to unify public finance platforms – often led by sovereign wealth funds or centralised finance ministries – highlight the growing need for shared standards and interoperable systems.
Capacity and culture gaps: AI deployment requires more than technical expertise. It demands a culture that embraces innovation and adaptive decision-making. Many institutions lack digital capabilities, face internal resistance to change, or operate within risk-averse environments where experimentation is discouraged. Regional actors such as the Arab Monetary Fund have highlighted the need for stronger institutional coordination and innovation ecosystems to advance digital finance transformation across the Arab region.
Ethics, security, and transparency concerns: As AI begins to shape sensitive fiscal decisions, such as allocating benefits or reallocating funds, issues of fairness, legality, and accountability become critical. Without clear rules, AI can produce biased outcomes or breach financial regulations. Weak cybersecurity may expose sensitive fiscal data, threatening national security and eroding trust. Public finance professionals and citizens must understand how AI insights are generated and used. Opaque algorithms or poorly communicated logic risk undermining both legitimacy and public confidence.
Absence of robust evaluation and ROI frameworks: AI returns are harder to measure and often intangible in the short term. This makes it challenging to prioritise and scale promising pilots. Without clear methodologies to assess impact – including efficiency gains, accuracy improvements, and equity outcomes – AI programmes struggle to secure sustained funding and political backing.
A strategic path forward
To move from pilots to purpose-driven AI adoption, governments should focus on five priorities:
Anchor AI in core fiscal strategy: Define AI priorities top-down, aligned with institutional and national fiscal and development goals. Focus on areas where AI advances mandates such as revenue mobilisation, spending efficiency, or compliance.
Invest in infrastructure and people: Build modern cloud infrastructure, hire skilled data engineers, and provide ongoing training to unlock AI's full value beyond isolated pilots.
Strengthen data governance: Establish strong data governance frameworks to improve data accessibility, quality, and interoperability, while safeguarding privacy and promoting ethical use.
Measure what matters: Track cost-benefit metrics alongside accuracy, compliance, equity, and public confidence to capture AI's true impact on fiscal management.
Embed safeguards: Require model transparency, independent audits, and clear accountability frameworks before AI tools influence high-stakes fiscal decisions.
Read:
The time to act is now
AI is not just a technological upgrade – it is a fundamental shift in managing public finance. Governments that embed it strategically will unlock unprecedented agility, precision, and transparency. Moving beyond advisory roles, prescriptive models can drive smarter, faster, and more accountable policy decisions.
But this power demands caution: without rigorous transparency, fairness, and accountability safeguards, such systems risk bias and unintended consequences that could undermine trust. Done responsibly, AI can help governments anticipate shocks, improve policy outcomes, and enhance public confidence.
For MENA countries, where fiscal reform and economic diversification are top priorities, the stakes are even higher. With the right investments, governance, and institutional commitment, the region can not only catch up, but lead in shaping the future of public finance.
Naman Sharma and Pedro Marques are partners, and Rayane Dandache is a manager at