Systematic Review of Financial Distress Prediction Models for Municipalities: Key Evaluation Criteria and a Framework for Model Selection
Bibliographic record
Abstract
Municipalities are facing mounting fiscal pressures that contribute to financial distress, often resulting in reduced service delivery and economic instability. Despite extensive research on this topic, there is neither a framework nor established criteria to guide policymakers and practitioners in selecting appropriate models for financial distress prediction (FDP). This study employs a systematic review approach to identify key criteria for evaluating FDP models and proposes a framework to guide the selection of suitable models. Following PRISMA guidelines, 24 peer-reviewed papers published between 2000 and 2025 were identified through Google Scholar, Web of Science, ScienceDirect, Scopus, EBSCOhost, and ProQuest. The analysis revealed ten key criteria for evaluating FDP models in local government, which were organised into four overarching dimensions: performance, conceptual integrity, practical applicability, and contextual fit. Based on these insights, the study proposes a structured framework that assists practitioners in selecting the most appropriate FDP model. The framework enhances conceptual clarity, synthesises fragmented knowledge, and establishes a foundation for policy-relevant early warning systems to strengthen municipal financial management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".