Financial technology adoption and bank stability among African economies: Is the relationship monotonic?
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DOIhttp://dx.doi.org/10.21511/imfi.21(4).2024.31
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Article InfoVolume 21 2024, Issue #4, pp. 385-399
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Many researchers attribute the vulnerability of African banks to poor innovation and technology adoption in the continent. While many studies suggest that Fintech adoption can mitigate instabilities/risks, this study argues that adopting Fintech brings both challenges and opportunities. Consequently, the study examines a monotonic connection between Fintech and bank stability in a panel of 26 African economies from 2004 to 2021. After measuring bank stability with the bank Z-score, the Principal Component Analysis (PCA) was employed to generate an index of Fintech using various digital payment indicators. The results of the System Generalized Method of Moments (GMM) technique reveal that the relationship is U-shaped in the short run but monotonic in the long run with greater magnitude. Hence, an oscillatory divergent relationship was implied for the entire period. That is, Fintech improves and worsens bank stability intermittently over time. The result is still valid with the inclusion of bank-specific and macroeconomic variables but it was improved with the inclusion of institutional variables in the model. Furthermore, the U-test analysis employed as a second-order robustness check for the U-shaped relationship confirms that Fintech adoption will first worsen bank stability before improving it. The study concludes that Fintech’s ability to improve bank stability depends on the extent and quality of institutional development/regulations in the region. The study therefore recommends institutional development and Fintech regulation to guarantee steady financial/bank stability through Fintech adoption.
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JEL Classification (Paper profile tab)G21, E44, G01, O33
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References44
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Tables7
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Figures1
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- Figure 1. Growth pattern of bank stability among African economies 2004–2021
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- Table 1. Data description, sources and measurement
- Table 2. Descriptive statistics
- Table 3. Pairwise correlation result
- Table 4. Short-run system GMM results based on equation (3)
- Table 5. Long-run results based on the short-rum system GMM outputs in Models 1-4
- Table 6. Results of the Lind & Mehlum test for the U-shaped relationship
- Table A1. Results of the principal component analysis for Fintech index
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