FinTech and Good Governance as Catalysts for Financial Sustainability in SSA: Unveiling the Asymmetric Impact of Exchange Rate Volatility

2.1 Fintech effects on financial sustainability

FinTech’s impact on bank performance is complex, with both positive and negative aspects. On the positive side, Fintech improves efficiency, attracts new customers, and offers better avenues for revenue generation and risk management. On the downside, it raises competition and disrupts the traditional business model, creating cybersecurity vulnerabilities and other regulatory complexities and challenges. A couple of key research findings indicate that FinTech has an overall adverse effect on banks’ profitability and competitiveness. As digitization is necessary, it would be better for banks to adapt financial technologies to compete. The results of a study conducted by Haoliang et al. (2024) conclude that FinTech has a lot more negative impacts on banks’ profitability and give pragmatic reasons for banks to adapt and innovate to the changes of FinTech. The practical implications of this study will revolve around the fact that the banks should not only invest in FinTech research and development but also customize strategic action following bank types, as proposed by the research findings of Haoliang et al. (2024).

The effects of FinTech on traditional banking are diverse in different areas or regions. During the year 2024, Haoliang et al. (2024) propose that the influence has been negatively consequential to the profitability of banks in China, while in other countries, such as Indonesia and Nepal, which were considered respectively by Hidayat (2024), Melati (2024) and Bhujel (2024), FinTech raises efficiency and performance. Fang and Wen (2024) also examined the collaborative aspects of banks with FinTech firms, and this can be productive if knowledge sharing, synergy, and perceived risks are carefully considered. As explored, collaboration between banks and FinTech firms can be beneficial but requires careful consideration of knowledge sharing, synergy, and perceived risks. Kharrat et al. (2024) studied the influence of FinTech development on the performance of banks in the Middle East and North Africa (MENA) region. According to the study, bank performance positively correlates with the FinTech index. This implies that FinTech implementation improves the performance of conventional and Islamic banks in that region. Naser et al. (2024) and Ajouz et al. (2024) also found a favorable association between FinTech innovation and all four dimensions of bank performance: marketing efficacy, financial robustness, operational efficiency and strategic competitiveness. The study of Safiullah and Paramati (2024) demonstrated a positive relationship between the development of FinTech companies and bank financial stability. It indicates that the growth of FinTech firms can enhance banks’ financial health.

2.2 Good governance effects on financial sustainability

The study by Fanta and Kemal [64] aimed to determine the effects of internal and external corporate governance mechanisms on the financial performance of commercial banks in Ethiopia. The study applied panel data econometric analysis and found that bank performance was negatively associated with board size and the presence of an audit committee, whilst positively associated with bank size and the capital adequacy ratio. However, there was a nonlinear relationship between the capital adequacy ratio and performance, suggesting an optimal capital adequacy level. The results also show that ownership type, loan loss provision, and loan-to-deposit ratio did not significantly affect performance. The objective of this research by Chughtai [65] was to explore the effect of corporate governance mechanisms on the financial performance of commercial banks in Pakistan. The study, using panel data analysis, reveals that larger boards and audit committees led to higher profitability and productivity gains, whereas inefficient boards and audit committees led to lower profitability and productivity. Findings were also that CEO duality did not have a significant effect on bank performance.

Nevertheless, when the same study applied performance measures, gender diversity harmed all of them. EPS and Technical Efficiency (TE) benefited from foreign ownership. Another study conducted in Bangladesh by Sathye [69] aimed to develop a unique corporate governance index and evaluate its impact on bank performance. According to the Wilcoxon signed-rank test, the findings indicate that introducing the Code of Corporate Governance significantly enhanced corporate governance practices. However, no significant relationship was observed in the regression analysis between the comprehensive corporate governance index and bank performance. Rashid et al. (2020) explored the impact of corporate governance on bank productivity in Bangladesh. In a two-step approach, bank productivity is first measured using the Malmquist Productivity Index, followed by regression analysis to identify the underlying factors driving it. The results show that ownership structure and other board characteristics under corporate governance are strong predictors of bank productivity. These facts have important implications for policymakers and bank managers when devising policies to improve bank productivity and overall economic growth.

2.3 Exchange rate movement effects on financial sustainability

The impact of exchange rates, interest rates, and global risk on each bank’s returns is covered in the scope of this study by Çiçek and Yıldırım (2024). Using the multivariate diagonal BEKK-GARCH(1,1) model, it is found that private banks exhibit relatively significant interest rate fluctuations, while public banks are insensitive to shocks. Another study finding is that the spillovers are significant, mainly due to global volatility factors. The findings indicate that the Turkish banking sector was more sensitive to domestic interest rate shocks than exchange or global shocks. This study found evidence of structural breaks in the interest rate covariance equations, indicating that any model of financial volatility must account for them. Yemi and Nakawooya (2024) analyzed the impact of foreign exchange rates on the profitability of Nigerian banks. A descriptive study design was used, and a Pearson correlation analysis was performed. Hence, data from 23 First Bank branches in Lagos and Ondo States were analyzed. From this, it was found that a higher foreign exchange rate positively influences bank profitability. This means that banks stand to gain through currency fluctuations, especially the devaluation of the home currency.

The study by Kush (2022) sought to ascertain how exchange rate fluctuations affect the performance of Centenary Bank Uganda Limited’s head office. This descriptive correlational survey research design was collected through interviews and questionnaires. The results show that the bank’s financial performance is primarily affected by its economic stability, monetary policies, and competitiveness in international trade. It indicates that the FED considers a stable economic environment conducive to good earnings, effective monetary policies, and a competitive international trade environment. Whereas Keshtgar et al. (2020) examined the effect of exchange rate fluctuations on the performance of Iranian banks. Panel data regression with random effects was employed to analyze data from 14 Iranian banks from 2007 to 2017. In other words, exchange rate volatility negatively affects bank profitability (return on investment) but positively affects the loan-to-deposit ratio. This means that exchange rate fluctuations can increase credit risk and reduce banks’ profitability. In addition, omitting bank-specific characteristics, including liquidity, equity and efficiency, can hinder the ability of exchange rate volatility to exert adverse impacts.

3.1 Theoretical foundation of the study

In the case of FinTech and Financial Sustainability, financial sustainability improves with FinTech innovations, which unlock greater efficiency and lower transaction costs. We can maximize resource utilization and improve operational performance by using FinTech solutions. However, the disposition between FinTech and financial sustainability might not be linear. Early stages of FinTech integration can increase uncertainty at the small scale and heighten the risk of disruptions. The Resource-Based View emphasizes internal resources and capabilities in generating competitiveness. The ability to use FinTech can become a major advantage to banks, whether implementing already available features (e.g., data analytics) or inventing new services (e.g., AI-based solutions), which will result in an increased competitive edge in dealing with customers, costs, risks, and so on (Ferilli et al., 2024). The agency theory is a useful perspective for aligning stakeholders’ interests. FinTech can help reduce the agency problem by boosting transparency and accountability, and by improving communication between banks and stakeholders. With institutional theory, on the other hand, the focus is on the role of external forces - regulation, societal pressures, and stakeholder interests - in organizational behavior on a more general level. FinTech, therefore, helps banks adapt to regulatory changes and evolving customer needs, thereby enhancing their long-term sustainability.

Good governance reduces agency costs by aligning management and shareholders. Independent boards are more effective at assessing long-term financial sustainability. Studies affirm that sound corporate governance reduces managerial opportunism and fosters decision-making aligned with social norms and profitable financial outcomes (Sulimany et al., 2021). One that dwells on the long-standing principle of stakeholder theory. This assists in establishing improved connections with customers, employees, and communities, enhances reputation, reduces risks, and increases the financial sustainability (Sulimany et al., 2021). Resource-Based View: Not only does Good governance help firms utilize their resources effectively, manage risks and create value, but it can also result in the firm achieving better long-run financial performance. Purchasing Power Parity (PPP) involves the exchange rates that are sensitive to the prices of goods. Fluctuating exchange rates affect pricing, profitability, and inflation. A devalued currency would lead to higher import costs, and profits would be limited until they are transported to the consumer. Interesting rates, exchange rates, output: The Mundell-Fleming model. Flexible exchange rates can influence financial viability relative to monetary policy, whereas fixed exchange rates cannot.

This study investigates the dynamic and asymmetric relationships among FinTech innovations, good governance, and financial sustainability, with a particular emphasis on the mediating role of exchange rate volatility (Qamruzzaman, 2026). The empirical model employs a nonlinear autoregressive distributed lag (NARDL) and cross-sectional ARDL (CS-ARDL) approach, capturing both long-run equilibria and short-run asymmetries.

The general form of the model is specified as:

Where: FS: Financial Sustainability is the dependent variable whose measures are proxied using such indices as the ratio of internal financing, return on assets, and net profit ratio. An increased FS reflects greater long-term financial resilience. $\mathit{\text{FINTEC}}{H}^{\{+\}},\mathit{\text{FINTEC}}{H}^{\{-\}}$ . Both positive and negative shocks in FinTech innovations can be quantified using indicators such as digital adoption, transaction volumes, and cost-efficiency metrics. It is assumed that 1 > 0: Positive FinTech shocks will enhance FS by operational efficiency, as well as financial inclusion. Furthermore, 2 < 0: FS can be hindered by adverse FinTech shocks, particularly technological turmoil or a lack of regulation. $G{G}^{\{+\}},G{G}^{\{-\}}$ : Good Governance change, positive or negative, based on regulatory quality, transparency and accountability indices. In the literature, 0 corresponds to 0.75x. If the entire policy process is more efficient and equitable in resource distribution, then improvements in governance are likely to increase the FS. Also, 02 0, implying declines in even governance, can lead to corrective institutional reactions of both positive and negative impacts in the short run. $\mathit{EX}{F}^{\{+\}},\mathit{EX}{F}^{\{-\}}$ : Exchange Rate Fluctuations are divided into positive shocks (appreciation) and negative shocks (depreciation). +: Positive shocks (appreciation) can reduce the costs of export competitiveness, thereby weakening FS. X: FDI and Trade Openness are control variables, which are likely to have a positive impact on FS when they are under stable macroeconomic environments, as indicated by Table 1.

3.2 Estimation strategies

This study applies a sequential panel estimation strategy (see Figure 1) to examine the relationships among FinTech development, good governance, exchange rate fluctuations and financial sustainability in Sub-Saharan Africa. The empirical design uses panel data for 25 Sub-Saharan African countries from 2004 to 2022. The dependent variable is financial sustainability (FS), while the main explanatory variables are FinTech development (FinTech), good governance (GG) and exchange rate fluctuation (EXf ). Foreign direct investment (FDI) and trade openness (TO) are included as control variables, consistent with the study’s data structure and variable definitions.

Figure 1. Estimation strategy.

The baseline functional relationship is specified as follows:

Before estimating the long-run model, the study tests slope heterogeneity and cross-sectional dependence. These tests are necessary because countries in Sub-Saharan Africa may differ in their financial structures, institutional capacities, exchange rate regimes and digital finance readiness. Ignoring these differences can produce biased estimates and misleading inferences.

The slope heterogeneity test follows Pesaran and Yamagata (2008), with the adjusted Delta statistic expressed as:

Cross-sectional dependence is examined using the Pesaran (2004) CD test. The test statistic is written as:

After confirming the panel structure, the study tests the integration order of each variable. Given the possibility of cross-sectional dependence, first-generation unit root tests may not be sufficient. Therefore, the study applies the cross-sectionally augmented Dickey-Fuller (CADF) and cross-sectionally augmented IPS (CIPS) tests developed by Pesaran (2007). The CADF regression is specified as:

The null hypothesis states that the variable contains a unit root. Rejection of the null confirms stationarity. Establishing the integration order is important because the CS-ARDL and NARDL frameworks require variables to be integrated at level, first difference, or a mixture of both, but not at second difference.

Once the integration properties are confirmed, the study tests for long-run cointegration. Cointegration confirms whether the variables move together over time despite short-run fluctuations. The study applies Westerlund (2007) error-correction-based panel cointegration tests because they are suitable for heterogeneous panels and can account for cross-sectional dependence. The error-correction form is:

The long- and short-run effects are then estimated using the cross-sectional autoregressive distributed lag (CS-ARDL) model developed by Chudik and Pesaran (2015). This estimator is appropriate because it controls for cross-sectional dependence by including cross-sectional averages of the dependent and independent variables. The CS-ARDL specification is:

The short-run CS-ARDL error-correction model is expressed as:

To capture asymmetric effects, the study uses the nonlinear autoregressive distributed lag (NARDL) model, following Shin et al. (2014). This model separates positive and negative changes in FinTech, governance and exchange rate fluctuation. The partial sum decomposition is specified as:

The asymmetric long-run model is specified as:

The corresponding NARDL error-correction model is:

This specification allows the study to determine whether positive and negative shocks in FinTech, governance and exchange rate fluctuation have different effects on financial sustainability. Long-run asymmetry is examined by testing:

Short-run asymmetry is tested through Wald restrictions on the differenced coefficients:

Rejection of these restrictions confirms asymmetric adjustment.

Finally, the study applies the Dumitrescu and Hurlin (2012) panel causality test to identify the direction of causal relationships among the variables. This test is suitable for heterogeneous panels because it allows causal effects to differ across countries. The causality model is expressed as:

The alternative hypothesis allows causality for at least some countries:

The average Wald statistic is computed as:

The standardized statistic is expressed as:

A significant statistic rejects the null of non-causality and confirms directional linkage among the variables. This final stage helps explain whether FinTech, governance and exchange rate fluctuation act as drivers of financial sustainability or whether financial sustainability also feeds back into these variables over time.

Estimation and interpretation

The slope heterogeneity (SH) test, see output in Table 2, developed by Bersvendsen and Ditzen (2021), examines whether relationships differ between groups or change over time. For the model, it is highly significant at the 1% level (*) on both the Delta Statistic (4.6529) and Adjusted Delta Statistic (5.4082), proving the effects of variables are not constant. These results reveal differential effects between groups, suggesting that such analyses should account for slope heterogeneity.

As shown in Table 3 below, the test variables exhibit considerable cross-sectional dependence. For all variables, the highest Breusch-Pagan statistics [110] were recorded for FS (346.615), EXf (330.299), and FINTECH (302.922), respectively. Under the assumption of cross-sectional dependence, the unit root assumptions affect Pesaran (2006) because the strength of the dependency differs across variables. While other tests reduced the residuals to values below the threshold for statistical significance, Juodis and Reese (2022) also report significant dependence.

Panel Unit Root Tests Results (see Table 4): most variables are nonstationary at the level. Due to this nonstationary regression, the results cannot be trusted and need to be corrected. However, once the first differencing has been applied, all variables are stationary, where the associated test statistics are highly statistically significant (*). This indicates that the variables are (1) order integrated.

The table on the panel cointegration test, see Table 5, presents the results from the cointegration tests by Westerlund and Edgerton (2008) test statistics, LMг and LMΦ, are statistically significant for all test scenarios (no shift, mean shift, and regime shift) (P < 0.05 or P < 0.01). This shows that these variables are cointegrated, meaning they have a long-run equilibrium relationship in the presence of the shifts. Particularly for model 1, LMг and LMΦ remain significant at −4.8688* (no shift), −4.7273* (mean shift), and − 3.3095* (regime shift), indicating that the variables are cointegrated over time. In addition, the Westerlund and Edgerton (2007) cointegration test, using Gt, Ga, Pt, and Pa test statistics, provides similar but strong evidence of a cointegrating relationship (results are reported in Table 1).

For Fintech, the long-run (short-run) coefficients in Table 6 indicate a positive association with financial sustainability, suggesting that financial expansion driven by technological innovation fosters financial sustainability. More precisely, a 1% positive change in FinTech will accelerate FS by 0.0923% (0.0534%). As for Exchange Rate Fluctuation (EXf ), the long-run coefficient is positive: a 1% increase in EXf is associated with a 0.0623% rise in financial sustainability. However, in the short term, EXf undermines financial sustainability: a 1% increase reduces it by 0.0298%. Our study is supported by Aizenman et al. (2024) and Yemi and Nakawooya (2024). GG has a positive relationship with long-run and short-term coefficients of financial sustainability. The long-run impact of a 1% increase in GG is an improvement of 0.0212% in FS, while the short-run increase is 0.0327%. This portion is supported by the literature of (Athar et al., 2023), (Tashkandi, 2023), and (Hoque et al., 2013). Concerning foreign direct investment (FDI), the findings confirm a significant positive impact on financial sustainability. A 1% increase in FDI results in 0.0584% (long-run) and 0.0571% (short-run) increase in financial sustainability. In contrast, the long-run and the short-run effects of Trade Openness (TO) are positive. Holding all else equal, a 1% increase in TO increases the financial sustainability by 0.0524% (long-run) and 0.0276% (short-run). Finally, the Error Correction Term (ECT) is significant and bears a negative value (−0.1499), which implies a high adjustment speed to the long-run equilibrium when deviation occurs. Approximately 14.99% of the disequilibrium is absorbed/semi–convergence in the short run.

Table 7 displays the asymmetric coefficients. As for FinTech, the positive (FinTech⁺) and negative (FinTech⁻) shocks have a significant pass-through effect in both the long and short run. Over the long run, a positive change in FinTech increases financial sustainability by 0.1112%, while a negative change decreases it by 0.1081%. Positive and negative shocks lessen financial sustainability by 0.0517% and 0.0508%, respectively, over the short term. This part is supported by Naser et al. (2024). Concerning Exchange Rate Fluctuation (EXf ), a positive shock leads to a drop in financial sustainability, FSD by 0.1117% (in the long-term) and by 0.0292% (in the short-term). A negative shock (EX⁻) has a greater effect than a positive one. It plays a role in the long term, decreasing financial sustainability by 0.1271%, while, more conveniently, but not yet fully, increasing sustainability by 0.011% in the short term. Our study is supported by Çiçek and Yıldırım (2024), Aizenman et al. (2024), and Yemi and Nakawooya (2024). In the case of Good Governance (GG), a positive change (GG⁺) leads to a 0.0978% improvement in long-run financial sustainability, while the short-run effect is 0.007%. This finding is supported by Rashid et al. (2020). The long-run impact of an adverse change (GG⁻) is much more pronounced and enhances financial sustainability by 0.1497%, whereas its short-run impact is insignificant (0.0009%). Furthermore, it is supported by Saha and Uddin (2024). This is evident in the long-run coefficient of Trade Openness (TO), which negatively affects financial sustainability by 0.1102%. It reduces financial sustainability by 0.0081% in the short term. Specifically, there is a strong long-run impact on Foreign Direct Investment (FDI), with a 1% increase leading to a 0.1506% improvement in financial sustainability. FDI also impacts positively on financial sustainability in the short run, increasing it by 0.0179%. The ECT is statistically significant at −0.1465, indicating that approximately 14.65% of the disequilibrium in financial sustainability returns to equilibrium within one period.

Based on the results of the causality test (see Table 8), a unidirectional relationship exists between FS and FinTech. FS influences Fintech [FS➔FinTech]. Similarly, a unidirectional relationship is observed between FS and EXf [FS ➔EXf], where FS influences EXf. Additionally, FS influences GG [FS➔GG)] and FDI [FS (FDI)], as per the results. Finally, FS influences TO [FS➔TO].

Conversely, bidirectional relationships can be found between GG and FS [FS🡰 ç➔GG], where both variables influence each other. Similarly, a bidirectional relationship exists between FinTech and EXf [FinTech➔EXf]. A bidirectional relationship is also seen between FDI and TO [FDI➔TO]. These results emphasize the complex, interdependent relationships among variables in promoting financial sustainability and economic development.