Enhancing financial performance and risk management in Kazakhstan’s banking sector
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Author(s)Perizat BuzaubayevaLink to ORCID Index: https://orcid.org/0000-0003-3726-482X
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Aigul OrazbayevaLink to ORCID Index: https://orcid.org/0000-0001-9633-1223
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Gulzhan Alina
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Zamzagul BaimagambetovaLink to ORCID Index: https://orcid.org/0000-0002-7509-0894
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Gulzhihan KengesLink to ORCID Index: https://orcid.org/0000-0001-8529-8923
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DOIhttp://dx.doi.org/10.21511/bbs.19(1).2024.14
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Article InfoVolume 19 2024, Issue #1, pp. 157-169
- 438 Views
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126 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
This study aims to assess the impact of regulatory compliance on the effectiveness of risk management and the financial performance of Kazakhstan’s banking sector. Applying Structural Equation Modeling (SEM), the study examines data from Kazakhstani banking institutions, revealing the direct and mediated impacts of regulatory compliance on financial performance, with risk management efficacy as a key intermediary. The analysis identifies a significant direct relationship between regulatory compliance and risk management efficacy (coefficient: 0.45, p-value: < 0.001), suggesting that compliance efforts substantially bolster risk management capabilities. The impact of risk management efficacy on financial performance is also notable (coefficient: 0.35, p-value: < 0.001), confirming its crucial role in financial success. Additionally, a direct, though less pronounced, influence of regulatory compliance on financial performance is observed (coefficient: 0.20, p-value: 0.004). The model’s explanatory power is reflected in an R-squared value of 0.248, indicating that it accounts for approximately 24.8% of the variability in financial performance. These findings underline the critical role of regulatory adherence and effective risk management in ensuring financial success, offering strategic insights for banking operations in Kazakhstan.
- Keywords
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JEL Classification (Paper profile tab)G21, G28, C58
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References59
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Tables6
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Figures0
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- Table 1. Descriptive statistics for study variables
- Table 2. Variance Inflation Factor (VIF) to test for multicollinearity
- Table 3. SEM model parameters and estimates
- Table 4. Fit indices
- Table 5. Regression analysis results
- Table 6. Model fit
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doi: http://dx.doi.org/10.21511/ppm.18(2).2020.03
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Samiul Parvez Ahmed ,
Ikramul Hasan
doi: http://dx.doi.org/10.21511/bbs.13(3).2018.07
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