Artificial intelligence strengthens fiscal transparency in public finance

A new study assesses how artificial intelligence (AI) is transforming local government financial reporting worldwide. Published in the Journal of Risk and Financial Management, the paper provides a critical assessment of how AI systems are streamlining reporting, enhancing data integrity, and promoting fiscal accountability in the public sector.

Titled "The Role of Artificial Intelligence in Improving the Efficiency and Accuracy of Local Government Financial Reporting: A Systematic Literature Review," the research systematically reviews 20 peer-reviewed studies published between 2017 and 2025, offering one of the most comprehensive analyses to date of AI's role in public finance. By integrating findings across global cases, the authors demonstrate that AI is reshaping how governments prepare, verify, and disclose financial data, while also exposing key challenges that impede widespread adoption.

AI's expanding role in local financial systems

The study sees a rapid surge in the use of AI technologies in financial management, particularly across local government institutions in Asia, where public sector digitalization is accelerating. Using a systematic literature review method, the authors categorize AI applications into four primary functions: predictive reporting, fraud detection, performance monitoring, and automation of accounting processes.

Machine learning, expert systems, and robotic process automation (RPA) have emerged as the most dominant tools used to process large datasets, identify financial anomalies, and forecast fiscal performance. Governments increasingly rely on these systems to generate timely and accurate reports while minimizing human error and resource waste.

According to the review, machine learning algorithms are used in several municipalities to predict revenue fluctuations, detect budget inconsistencies, and flag fraudulent transactions before they escalate. Expert systems, computer programs that mimic human judgment, assist auditors and financial controllers in evaluating policy compliance and expenditure alignment. The integration of big data analytics and AI-driven dashboards allows policymakers to visualize spending patterns and performance indicators in real time, improving the speed and reliability of decision-making.

These technologies not only increase efficiency but also strengthen transparency and accountability. By reducing manual data entry and improving audit trails, AI-based systems make it easier for regulators, auditors, and citizens to access and verify public financial information.

However, the authors warn that despite these gains, AI adoption remains uneven across regions. Many local governments still lack the digital infrastructure and skilled personnel required to manage and interpret AI outputs.

Bridging the gap between technology and governance

The study further addresses how AI can be integrated responsibly into local government systems without compromising ethical standards, fairness, and data privacy. The authors find that while interest in AI for public finance has grown sharply since 2023, practical implementation remains limited.

Their analysis reveals that eight of the reviewed studies focused on machine learning applications, while four explored expert systems and only a few examined RPA and intelligent agents. This imbalance indicates that governments are still experimenting with traditional AI methods, leaving newer approaches, such as deep learning and cognitive computing, underutilized in public finance.

The study underscores that regulatory frameworks have not kept pace with technological advances. Many local governments have adopted AI tools without clear governance guidelines, increasing the risk of biased algorithms, data misuse, and cybersecurity vulnerabilities. The absence of standardized policies for data handling and algorithm transparency also raises concerns about public trust.

Moreover, AI's reliance on large datasets introduces potential ethical and legal challenges, particularly when financial and personal data intersect. Inaccurate data training or poor oversight can lead to flawed predictions or discriminatory outcomes in financial decision-making.

To mitigate these risks, the authors propose establishing robust AI governance models that balance automation with human oversight. They suggest developing interdisciplinary frameworks combining data science, accounting ethics, and public administration to ensure responsible AI implementation.

The study also calls for capacity building among local financial officers to enhance digital literacy and reduce dependence on external consultants. Training programs in AI auditing and system maintenance would help local governments build long-term self-reliance in digital reporting.

Challenges, research gaps, and the road ahead

AI adoption in local government finance faces persistent structural and institutional barriers. The authors identify three primary challenges: infrastructure readiness, regulatory inconsistency, and organizational inertia.

Many municipalities, especially in developing regions, still operate with outdated accounting systems that cannot accommodate AI integration. Budget constraints and cybersecurity concerns further limit experimentation with cloud-based platforms or real-time analytics tools. As a result, while the research volume on AI in public finance has increased, tangible on-the-ground implementations remain scarce.

The review also highlights several research gaps. Few studies explore how AI can improve audit efficiency, cybersecurity resilience, or data-driven policymaking. Even fewer evaluate the long-term social and environmental implications of AI-enabled financial governance. The authors recommend future studies to develop hybrid frameworks that integrate AI with existing accounting information systems to support adaptive learning and continuous improvement.

Importantly, the authors advocate for cross-sector collaboration among academia, policymakers, and technology providers. Such partnerships would accelerate the creation of open-access data platforms and shared AI solutions tailored to local government needs. International cooperation could also help harmonize data standards, enabling governments to exchange best practices and jointly address ethical and cybersecurity concerns.

Moving ahead, the paper envisions a future where AI-powered financial reporting becomes a cornerstone of transparent governance. By embedding predictive analytics, anomaly detection, and real-time monitoring into everyday fiscal management, governments can make better, faster, and more accountable financial decisions. However, realizing this vision requires more than technology, it demands policy alignment, ethical oversight, and human capacity development.