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Hierarchical clustering-based early warning model for predicting bank failures: Insights from Ghana's financial sector reforms (2017–2019)

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  • Owoo, Natalia
  • Odei-Mensah, Jones

Abstract

Between 2017 and 2019, Ghana experienced extensive financial sector reform in response to a banking sector crisis which led to the revocation of licenses for numerous banks and lower-tier institutions, and substantial financial losses. This study identifies factors that led to the collapse of these institutions as part of an early warning model (EWM) to identify weak institutions which are likely to fail. Employing hierarchical clustering, an underutilised unsupervised exploratory technique, we identify common explanatory variables that distinguished failed banks from their surviving counterparts. Our analysis underscores the significance of earnings and profitability indicators in effectively differentiating failed banks, surpassing other traditional CAMEL and diversification metrics.

Suggested Citation

  • Owoo, Natalia & Odei-Mensah, Jones, 2025. "Hierarchical clustering-based early warning model for predicting bank failures: Insights from Ghana's financial sector reforms (2017–2019)," Research in International Business and Finance, Elsevier, vol. 77(PB).
    • Handle: RePEc:eee:riibaf:v:77:y:2025:i:pb:s0275531925002004
    DOI: 10.1016/j.ribaf.2025.102944
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