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

Petr Jakubík and Saida Teleu
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Saida Teleu: Central Bank of Barbados, Tom Adams Financial Centre, Spry Street, Bridgetown 11126, Barbados

Risks, 2025, vol. 13, issue 2, 1-20

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.

Keywords: expected credit loss; stress testing; IFRS 9 (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2025
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