Systematic Review of Financial Distress Prediction Models for Municipalities: Key Evaluation Criteria and a Framework for Model Selection

Nkosinathi Emmanuel Radebe (), Bomi Cyril Nomlala and Frank Ranganai Matenda
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Nkosinathi Emmanuel Radebe: School of Accounting, Economics and Finance, College of Law and Management Studies, University of KwaZulu-Natal, Durban 4041, South Africa
Bomi Cyril Nomlala: School of Accounting, Economics and Finance, College of Law and Management Studies, University of KwaZulu-Natal, Durban 4041, South Africa
Frank Ranganai Matenda: School of Accounting, Economics and Finance, College of Law and Management Studies, University of KwaZulu-Natal, Durban 4041, South Africa

JRFM, 2025, vol. 18, issue 11, 1-33

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.

Keywords: municipal financial distress; budgeting; public finance; early warning systems; financial management; machine learning; prediction models; local government; systematic literature review (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2025
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