Financial technology development: Implications for traditional banks in Africa
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DOIhttp://dx.doi.org/10.21511/imfi.20(3).2023.14
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Article InfoVolume 20 2023, Issue #3, pp. 166-176
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The speed of financial technology (Fintech) adoption in delivering financial services has raised concerns among researchers on the future of traditional banks, especially as authors believe that Fintech comes with both prospects and problems. This study therefore aims to examine the growth, measurements, and the impact of Fintech on traditional banks in a panel of sixteen African countries for the period 1800–2020. These periods were divided into three phases: the analogue (1800–1967), the digital (1967–2008), and the modern phases (2008–2020). The autoregressive distributed lag (ARDL) and descriptive analyses methods were used to investigate the study’s objectives. It found that the analogue era witnessed the birth of Fintech ideas, while the digital era witnessed structural changes within the financial system. Results from the pooled mean group ARDL estimation technique based on the third/modern era reveal that, on average, a unit increase in Fintech adoption significantly reduces bank profitability (ROA) by 12.6%. Hence, although early Fintech adoption poses no threat to bank profitability; however, beyond certain threshold, its continuous adoption reduces profitability. Again, the speed of adjustment at 90.9% per annum is an indication that short-run Fintech disruptive impact/disequilibrium is corrected within one year and one month. The Principal Component Analysis used to generate Fintech index shows that African Fintech’s operation is more susceptible to changes in mobile banking. The study concludes that too much Fintech adoption is unhealthy for traditional banks in Africa and therefore it recommends that Fintech should collaborate with banks to correct for its disruptive impacts.
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JEL Classification (Paper profile tab)G00, G20, G23, G24
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References29
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Tables3
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Figures0
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- Table 1. A Fintech Index generated with the Principal Component Analysis
- Table 2. Unit root test results
- Table 3. ARDL model estimators of PMG and MG results
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