The Role of Artificial Intelligence in Improving the Efficiency and Accuracy of Local Government Financial Reporting: A Systematic Literature Review
Bibliographic record
Abstract
Digital transformation has driven the use of artificial intelligence (AI) in local government financial reporting to improve efficiency, transparency, and accountability. This study employs a systematic literature review (SLR) approach to analyze 20 relevant articles, identifying common characteristics of publications, research focus, methods, AI technologies used, key findings, research gaps, and future research directions. The analysis results show the dominance of machine learning and expert systems in detecting fraud, predicting financial performance, and improving reporting accuracy. However, limitations in infrastructure, regulations, and system integration across government agencies remain significant challenges to implementing AI in the public sector. This study proposes the need for the development of practical implementation models, collaboration between academics, government, and technology developers, as well as the formulation of policies that support ethical and responsible AI governance. These findings make a significant contribution to shaping the strategic direction of AI utilization to strengthen local government financial reporting systems sustainably.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.028 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".