The Impact of Financial Technology (FinTech) on Bank Risk-Taking and Profitability in Small Developing Island States: A Study of Fiji
Abstract
1. Introduction
2. Literature Review and Hypothesis Development
2.1. Linking FinTech and Bank Risk-Taking
2.2. Linking FinTech and Bank Profitability
3. Data, Variable Definition, and Method
3.1. Data
3.2. Dependent Variable
3.3. Independent Variables
3.4. Control Variables
- (a)
- Bank size: Larger banks often benefit from diversification across regions and asset types, stronger risk management systems, and better access to capital, which can reduce their overall risk (Adusei, 2015). They may also be perceived as “too big to fail”, which can provide stability through implicit government support (Kaufman, 2014). However, this size also brings complexity, making them harder to monitor and more exposed to systemic risk (Varotto & Zhao, 2018). Empirical studies such as Kayed et al. (2025) and Martínez-Malvar and Baselga-Pascual (2020) found that there are positive effects of bank size on bank risk. In contrast, Sari and Hanafi (2025) found a negative relationship between bank size and bank risk. Hence, the impact of bank size on bank risk-taking is ambiguous.Regarding the relationship between bank size and bank profitability, larger banks often benefit from economies of scale, greater market power, and diversification, leading to higher profitability through lower costs and more revenue opportunities (Baltensperger, 1972). Studies such as Kayed et al. (2025) and Chand et al. (2024b) found a positive effect of bank size on bank profitability. Therefore, we hypothesize that size has a positive effect on bank profitability.
- (b)
- Leverage ratio: On one hand, greater financial leverage increases a bank’s vulnerability to shocks, reduces its equity buffer, and heightens the risk of insolvency and liquidity crises, especially during periods of stress (Kalemli-Ozcan et al., 2012). This view, grounded in theories like the trade-off theory, agency theory, and moral hazard, suggests that excessive reliance on liabilities may incentivize risk-taking behavior, particularly if banks expect deposit insurance or government bailouts (Istiak & Serletis, 2020). On the other hand, proponents of efficient capital structures argue that some level of higher leverage can enhance returns and support growth, provided that risks are well-managed and the bank maintains high-quality assets (DeAngelo & Stulz, 2013). Hence, the impact of leverage on bank risk-taking is ambiguous.On the profitability aspect, high financial leverage may signal increased borrowing or deposit inflows, which can be profit-enhancing if the funds are used to finance high-return loans or investments (Beltratti & Paladino, 2015). Conversely, if the cost of the liabilities, such as interest paid on deposits or borrowings, exceeds the return on assets, it can erode profit margins and lead to losses. Consequently, the effect of bank leverage on bank profitability is ambiguous.
- (c)
- Gearing ratio: A higher gearing ratio implies more reliance on debt, leading to increased fixed obligations, reduced loss of absorption capacity, and heightened sensitivity to interest rate fluctuations, thus elevating the risk of financial distress (Bevan & Danbolt, 2002). Conversely, a lower gearing ratio suggests a stronger equity base, providing a greater buffer against losses and enhanced financial flexibility (Denis & McKeon, 2012). Hence, we postulate a positive association between bank risk-taking and gearing ratio.In terms of profitability, a higher gearing ratio can amplify returns on equity during profitable times, as borrowed funds can generate income exceeding borrowing costs (Mahmud et al., 2016). However, it also inflates interest expenses, directly decreasing profits and increasing vulnerability to economic downturns and interest rate hikes. Empirical studies such as Tulung et al. (2024) reported a positive impact of gearing ratio on bank profitability. Hence, we hypothesize that the gearing ratio is positively associated with bank profitability.
- (d)
- Liquidity ratio: A high liquidity ratio implies a larger portion of deposits is tied up in less liquid loans, increasing the probability of failing to meet sudden depositor withdrawals and potentially leading to asset fire sales or costly emergency borrowing (Acharya & Naqvi, 2012). On the other hand, a low ratio suggests greater readily available funds relative to loans, indicating lower liquidity risk. Hence, we hypothesize that the liquidity ratio is positively associated with bank risk-taking.On the profitability side, higher liquidity implies more deposits are deployed into interest-generating loans, the primary income source for banks (Kashyap et al., 2002). Conversely, a lower ratio reduces lending income, potentially lowering profits, but enhances liquidity. The empirical evidence regarding the impact of liquidity ratio on bank profitability is ambiguous. Studies such as Adelopo et al. (2018) and Hamdi and Hakimi (2019) found a negative relationship, while Rahman et al. (2015) reported positive effects of liquidity ratio on bank performance. Subsequently, the impact of the liquidity ratio has an ambiguous effect on bank performance.
- (e)
- Concentration ratio: Under the conventional “competition-fragility” hypothesis, increased bank competition reduces market power, diminishes franchise value, and thereby incentivizes banks to engage in greater risk-taking behavior (Kabir & Worthington, 2017). In contrast, under the conventional “competition-stability” perspective, greater market power in the loan market may elevate bank risk (Kabir & Worthington, 2017). Higher interest rates charged to borrowers under less competitive conditions can lead to repayment difficulties, thereby intensifying problems of moral hazard and adverse selection (Boyd & De Nicoló, 2005). Hence, the impact of the concentration ratio on bank risk-taking is ambiguous.With regard to bank profitability, high concentration, where a few large banks dominate, can enhance profitability through increased market power, allowing for wider interest rate spreads and potentially lower operating costs due to reduced competitive pressure (Heggestad, 1977). Studies such as Islam and Nishiyama (2016) and Abel et al. (2023) reported a positive association between bank concentration and bank profitability. Hence, we hypothesize that the bank concentration ratio is positively associated with bank profitability.
3.5. Empirical Model
4. Results and Discussion
4.1. Descriptive Analysis and Correlation Analysis
4.2. Regression Analysis and Discussion
4.3. Robustness Check
5. Conclusions and Policy Implications
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
1 | BSP was initially operated as Colonial bank. |
2 | HFC was initially operated as credit institutions. |
3 | Charter value can be understood as the present value of a bank’s expected future profits (Demsetz et al., 1996). |
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Variable | Definition | Symbol | Some Evidence from Past Studies | Data Source |
---|---|---|---|---|
Dependent Variables | ||||
Bank risk-taking | Z-score consists of ROA, equity-total assets, and standard deviation of ROA | RISK | Kayed et al. (2025); Hafeez et al. (2022); R. Wang et al. (2021) | RBF |
Bank profitability | Net profits after tax divided by total assets | ROA | Kayed et al. (2025); Chand et al. (2024b); Le and Ngo (2020); Yüksel et al. (2018) | RBF |
Independent Variable | ||||
FinTech | Mobile banking | FINTECH | Kayed et al. (2025); Alghadi (2024); J. Kumar and Rani (2024) | RBF |
Control Variables | ||||
Bank size | Natural logarithm of total assets | BSIZE | Kayed et al. (2025); Sari and Hanafi (2025); Martínez-Malvar and Baselga-Pascual (2020) | RBF |
Leverage ratio | Total liabilities divided by total assets | LEV | Kayed et al. (2025); Zhang et al. (2025); Fatouh et al. (2024) | RBF |
Gearing ratio | Total liabilities divided by total equity | GEA | Kayed et al. (2025); Lusy et al. (2018) | RBF |
Liquidity ratio | Total loans divided by total deposits | LIQ | Adelopo et al. (2018); Rahman et al. (2015) | RBF |
Concentration ratio | Herfindahl–Hirschman index based on loan | HHI | Abel et al. (2023); Islam and Nishiyama (2016) | RBF |
RISK | ROA | FINTECH | BSIZE | LEV | GEA | LIQ | HHI | |
---|---|---|---|---|---|---|---|---|
Mean | 14.28 | 0.03 | 0.27 | 12.96 | 0.86 | 8.44 | 1.06 | 2485.01 |
Median | 13.42 | 0.02 | 0.00 | 13.11 | 0.90 | 8.91 | 0.94 | 2599.35 |
Maximum | 52.64 | 0.12 | 1.00 | 15.02 | 0.96 | 27.21 | 12.72 | 2928.96 |
Minimum | 4.03 | −0.03 | 0.00 | 10.64 | 0.01 | 0.01 | 0.02 | 1879.50 |
Std. Dev. | 8.01 | 0.02 | 0.45 | 1.28 | 0.11 | 4.43 | 1.04 | 343.44 |
Skewness | 1.20 | 1.20 | 1.01 | −0.08 | −3.85 | 0.55 | 8.77 | −0.59 |
Kurtosis | 5.70 | 5.29 | 2.02 | 1.72 | 28.86 | 4.04 | 98.60 | 1.99 |
Probability | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
Observations | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 |
RISK | ROA | FINTECH | BSIZE | LEV | GEA | LIQ | HHI | |
---|---|---|---|---|---|---|---|---|
RISK | 1 | 0.17 | 0.13 | −0.34 | −0.42 | −0.30 | −0.07 | −0.05 |
ROA | 0.17 | 1 | −0.15 | −0.38 | −0.64 | −0.54 | 0.20 | 0.12 |
FINTECH | 0.13 | −0.15 | 1 | 0.45 | 0.05 | −0.15 | −0.12 | −0.63 |
BSIZE | −0.34 | −0.38 | 0.45 | 1 | 0.53 | 0.38 | −0.05 | −0.40 |
LEV | −0.39 | −0.65 | 0.04 | 0.53 | 1 | 0.45 | −0.19 | −0.02 |
GEA | −0.30 | −0.53 | −0.15 | 0.38 | 0.66 | 1 | −0.21 | 0.22 |
LIQ | −0.07 | 0.20 | −0.12 | −0.05 | −0.19 | −0.21 | 1 | 0.17 |
HHI | −0.05 | 0.12 | −0.83 | −0.40 | −0.01 | 0.22 | 0.17 | 1 |
-Stat. | -d.f. | Prob. | |
---|---|---|---|
Model: 1 | 81.14 | 5 | <0.01 |
Model: 2 | 67.41 | 5 | <0.01 |
Independent Variable | Coefficient | Standard Error |
---|---|---|
FINTECH | −1.1342 ** | 0.5762 |
BSIZE | 0.8528 *** | 0.3075 |
LEV | −49.627 *** | 2.4882 |
GEA | −0.4947 *** | 0.0811 |
LIQ | 0.1927 | 0.1496 |
HHI | −0.0001 | 0.0007 |
Constant | 47.9003 *** | 4.9491 |
Adjusted R-squared | 0.9488 | |
F-statistic | 269.9640 | |
Durbin-Watson stat | 0.8790 | |
Observations | 175 |
Independent Variable | Coefficient | Standard Error |
---|---|---|
FINTECH | 0.0055 *** | 0.0018 |
BSIZE | 0.0036 * | 0.0019 |
LEV | −0.1145 | 0.1168 |
GEA | −0.0006 | 0.0006 |
LIQ | 0.0001 | 0.0011 |
HHI | 0.0054 ** | 0.0024 |
Constant | 0.0456 | 0.0376 |
Adjusted R-squared | 0.5267 | |
F-statistic | 15.0154 | |
Durbin-Watson stat | 1.2301 | |
Observations | 175 |
Model 1. (Dependent Variable RISK) | Model 2. (Dependent Variable ROA) | |
---|---|---|
FINTECH | −7.6655 *** (2.1895) | 0.0047 *** (0.0016) |
BSIZE | 2.1012 *** (0.5758) | 0.0022 * (0.0014) |
LEV | −23.8408 *** (8.7174) | −0.1037 *** (0.0188) |
GEA | −0.1164 * (0.0654) | −0.0009 * (0.0004) |
LIQ | 0.7489 (0.5809) | 0.0004 (0.0012) |
HHI | −0.0035 (0.0027) | 0.0222 (0.0181) |
Constant | 51.372 *** (10.627) | 0.1029 *** (0.0224) |
Adjusted R-squared | 0.2729 | 0.4710 |
F-statistic | 10.5139 | 24.938 |
Durbin–Watson stat | 1.192 | |
Observations | 175 | 175 |
Model 1. (Dependent Variable RISK) | Model 2. (Dependent Variable ROA) | |
---|---|---|
FINTECH | −0.2089 * (0.1111) | 0.0075 * (0.0044) |
BSIZE | 0.9958 *** (0.2976) | 0.0027 * (0.0015) |
LEV | −49.3688 *** (2.4323) | −0.1063 *** (0.0190) |
GEA | −0.4368 *** (0.0756) | 0.0002 (0.0005) |
LIQ | 0.1170 (0.1481) | 0.0002 (0.0012) |
HHI | −0.0121 (0.0137) | 0.0014 * (0.0007) |
Constant | 46.9919 *** (4.4249) | 0.0850 *** (0.0301) |
Adjusted R-squared | 0.7751 | 0.2470 |
F-statistic | 103.1507 | 10.5131 |
Durbin–Watson stat | 0.5298 | 1.2402 |
Observations | 175 | 175 |
Model 1. (Dependent Variable RISK) | Model 2. (Dependent Variable ROA) | |
---|---|---|
L.RISK | 0.0161 (0.0131) | - |
L.ROA | - | 0.0058 * (0.0032) |
FINTECH | −1.1218 *** (0.3445) | 0.0541 *** (0.0181) |
BSIZE | 1.2766 *** (0.3920) | 0.0348 * (0.0207) |
LEV | −42.6414 ** (17.437) | −0.0444 ** (0.0211) |
GEA | −0.8328 * (0.4819) | 0.0019 (0.0056) |
LIQ | 0.2364 * (0.1401) | 0.0004 * (0.0017) |
HHI | −0.0001 (0.0045) | 0.0002 ** (0.0001) |
Constant | 0.4436 *** (0.1531) | 0.3591 (0.2943) |
Observations | 168 | 168 |
Hensen test | 27.47 | 23.24 |
Sargan test | 327.88 | 305.42 |
p-value of Sargan test | 0.121 | 0.387 |
Arrellano and Bond test AR (1) | −2.65 | −3.95 |
p-value of AR (1) | 0.008 | 0.000 |
Arrellano and Bond test AR (2) | 0.34 | 1.04 |
p-value of AR (2) | 0.734 | 0.300 |
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Chand, S.A.; Singh, B.; Narayan, K.; Chand, A. The Impact of Financial Technology (FinTech) on Bank Risk-Taking and Profitability in Small Developing Island States: A Study of Fiji. J. Risk Financial Manag. 2025, 18, 366. https://doi.org/10.3390/jrfm18070366
Chand SA, Singh B, Narayan K, Chand A. The Impact of Financial Technology (FinTech) on Bank Risk-Taking and Profitability in Small Developing Island States: A Study of Fiji. Journal of Risk and Financial Management. 2025; 18(7):366. https://doi.org/10.3390/jrfm18070366
Chicago/Turabian StyleChand, Shasnil Avinesh, Baljeet Singh, Krishneel Narayan, and Anish Chand. 2025. "The Impact of Financial Technology (FinTech) on Bank Risk-Taking and Profitability in Small Developing Island States: A Study of Fiji" Journal of Risk and Financial Management 18, no. 7: 366. https://doi.org/10.3390/jrfm18070366
APA StyleChand, S. A., Singh, B., Narayan, K., & Chand, A. (2025). The Impact of Financial Technology (FinTech) on Bank Risk-Taking and Profitability in Small Developing Island States: A Study of Fiji. Journal of Risk and Financial Management, 18(7), 366. https://doi.org/10.3390/jrfm18070366