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Improving Credit Risk Assessment in Uncertain Times: Insights from IFRS 9

Author

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  • Petr Jakubik

    (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Smetanovo nabrezi 6, 110 01 Prague 1, Czech Republic)

  • Saida Teleu

    (Central Bank of Barbados, Tom Adams Financial Centre, Spry Street, Bridgetown 11126, Barbados
    Department of Accounting and Finance, The School of Business, Anglo-American University in Prague, Letenská 120/5, 118 00 Prague 1, Czech Republic)

Abstract

This study highlights the superior performance of Bayesian Model Averaging (BMA) in credit risk modeling under IFRS 9, particularly during economic uncertainty, such as the COVID-19 pandemic. Using granular bank-level data from Malta, spanning 2017–2023, the analysis integrates macroeconomic scenarios and sector-specific transition matrices to assess credit risk dynamics. Key findings demonstrate BMA’s ability to outperform Single-Equation Models (SEM) in predictive accuracy, robustness, and adaptability. The results emphasize BMA’s resilience to structural economic changes, making it a critical tool for regulatory stress testing and provisioning in small open economies highly exposed to external shocks. This work underscores the importance of forward-looking, flexible frameworks for credit risk management and policy decisions.

Suggested Citation

  • Petr Jakubik & Saida Teleu, 2025. "Improving Credit Risk Assessment in Uncertain Times: Insights from IFRS 9," Risks, MDPI, vol. 13(2), pages 1-20, February.
    • Handle: RePEc:gam:jrisks:v:13:y:2025:i:2:p:38-:d:1595063
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    References listed on IDEAS

    as
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