Rizky Yudaruddin,wahyoe soedarmono,Bramantyo Adi Nugroho,Zhikry Fitrian,Mardiany Mardiany,Adi Hendro Purnomo,Eka Nor Santi
Financial technology and bank stability in an emerging market economy
Financial technology,Online peer-to-peer lending,Online payment,Bank stability
Using a sample of 141 banks in Indonesia during the 2004–2018 period, we investigate the impact of financial technology (FinTech) firms on bank stability. We find that more FinTech firms tend to enhance bank stability regardless of types of FinTech firms and the measurement of bank stability. Meanwhile, we also find that small banks and non-listed banks tend to benefit more from the presence of FinTech firms. Specifically, small banks and non-listed banks exhibit lower riskiness and higher capital ratios following an increase in the number of FinTech firms. This paper, therefore, emphasizes that FinTech development might be beneficial for financial stability, particularly when FinTech companies with small banks or non-listed banks.
Financial technology and bank stability in an emerging market economy

Using a sample of 141 banks in Indonesia during the 2004–2018 period, we investigate the impact of financial technology (FinTech) firms on bank stability. We find that more FinTech firms tend to enhance bank stability regardless of types of FinTech firms and the measurement of bank stability. Meanwhile, we also find that small banks and non-listed banks tend to benefit more from the presence of FinTech firms. Specifically, small banks and non-listed banks exhibit lower riskiness and higher capital ratios following an increase in the number of FinTech firms. This paper, therefore, emphasizes that FinTech development might be beneficial for financial stability, particularly when FinTech companies with small banks or non-listed banks.

Understanding the link between financial technology (FinTech) and financial stability is important for financial sector regulators. Financial Stability Board [1] highlights systemic problems from technological activities that contain operational and cyber risks, which may in turn adversely affect financial stability. However, FinTech development also enables greater innovations that provide more efficient financial services than traditional banking activities, and hence, the banking industry might suffer from the presence of FinTech [2]. For instance, FinTech can substitute traditional banking services, through online peer-to-peer (P2P) lending platforms without strict credit standards imposed on borrowers [3]. Consequently, bank supervisors need regulatory frameworks to ensure bank safety and soundness in addition to enhancing consumer protections by mitigating the risk of competition between FinTech and banks. Recent studies show that the financial industry is increasingly complex and dynamic [4], and it is vital to continuously examine the changing dynamics of financial institutions and their interactions with emerging technologies. The findings highlight the need for tighter regulations for technology firms, similar to those for banks [[5], [6], [7], [8]]. Therefore, in order to gain a complete understanding of the impact of FinTech on the banking industry and devise effective policies to ensure financial system stability, it is imperative to conduct a study on the impact of FinTech companies. As technology continues to disrupt the financial industry, it is essential to ensure that financial institutions remain resilient and capable of adapting to new challenges. In this paper, we explore the link between FinTech and bank stability. Thakor [9] builds a theoretical model to show that financial innovation will increase if the financial system is more competitive because of lower entry costs. However, more innovative financial systems are also more vulnerable to financial crises. Hence, we build on the work of Thakor [9] to empirically assess the role of FinTech as a proxy for financial innovation in affecting bank stability. To the best of our knowledge, very few papers are devoted to studying the impact of FinTech on bank stability (e.g. Refs. [10,11]). Hence, this paper aims to enrich the FinTech-bank stability literature by differentiating types of FinTech firms in influencing bank stability. Indeed, previous studies have examined the role of FinTech on bank behavior in developed countries, but they do not specifically investigate the impact of FinTech on bank stability [3,[12], [13], [14]]. Only Phan et al. [15] examine a closely related issue by investigating the impact of FinTech firms on the profitability of listed banks in Indonesia where FinTech development is negatively linked to bank profitability. However, Phan et al. [15] do not differentiate types of FinTech firms or banks, while the impact of FinTech development of bank stability also remains unexplored. Our study on the link between FinTech and bank stability is focused on the Indonesian context for several reasons. First, Indonesian banking plays an important role in influencing the global banking performance and stability. This is because the performance of Indonesian banking outweighs that of Asia-Pacific's banking industry, while Asia-Pacific's banking industry has also outperformed global banking for many years in terms of profitability [16]. Specifically, it is shown that return on average equities (ROAE) in Indonesian banking can reach 13.2% in 2018, while ROAE in banking in the Asia-Pacific region including developed markets and emerging markets only reaches 10.1% in average up to 2018. In this regard, Indonesian banking might affect global banking stability. Second, as an emerging market economy, Indonesia exhibits a continuous increase in the number of FinTech firms from year to year as in Fig. 1. Hence, assessing the impact of FinTech on Indonesian banking stability is relevant to provide empirical evidence for other countries with bank-based financial system to deal with FinTech development. The paper is organized as follows. Section 2 discusses related literature on the impact of FinTech on bank behavior, while we also present our research focus. Section 3 describes our data, variables, and empirical model. Section 4 provides our empirical findings and related discussions with some robustness checks. Section 5 concludes and provides policy implications.

According to Christensen [17], successful newcomers can target unexplored segments. Related to FinTech development, this theory shows that FinTech firms play in the small loan segments that do not create enough profit for traditional banks. Haddad and Hornuf [18] provide empirical evidence from a cross-country and highlight that higher degree of FinTech development is more pronounced for well-developed countries and countries with more venture capitalists. In addition, better technological infrastructures, higher labor force, and lower access to bank loans are associated with higher number of FinTech firms. In general, FinTech firms are IT (information technology) related firms that can disrupt traditional banking through new alternatives in increasing efficiency and service quality [19]. In parallel, Gomber et al. [20], also highlight some channels of disruption due to FinTech development. For instance, FinTech companies offer new products and solutions that meet customer needs that were not previously available by traditional banking services. Yet, FinTech firms have different culture and working environments compared to banking. Moreover, Pollari [21] argues that the notion that more FinTech firms increase competition in the financial sector is often exaggerated. This is because the substantial number of FinTech firms tends to build partnerships with financial institutions, while only mature FinTech firms may disrupt incumbents in the financial sector. Ozili [22] also points out a major issue associated with FinTech firms in which they often attract high-risk customers who are denied by banks. Hence, greater competition with banking is unlikely to occur due to FinTech development. Several recent studies are examining the interactions between financial institutions, particularly in response to technological advances and economic uncertainty. Bilgin et al. [5], show that economic uncertainty increases the default risk of conventional banks, but not that of Islamic banks. Meanwhile, Li et al. [23] examine the risk spillovers between FinTech and traditional financial institutions, finding that linkages are stronger during bearish conditions, leading to an increase in systemic risk for financial institutions. Pacelli et al. [24] focus on the European context and find that tail risk spillovers are more significant in downside conditions, with most spillovers occurring from banks to FinTech firms. Finally, Chaudhry et al. [25] evaluate the tail and systemic risk of technology firms, showing that their tail risk is higher than that of financial firms, although they are less likely to be in distress after a system shock. In relation to financial system stability, Thakor [9] postulates that financial innovation makes banks more risky and vulnerable to financial crises. A recent study by Buchak et al. [12], focusing on shadow banking market development in US residential mortgages is in line with Thakor [9]. They highlight that FinTech firms have also contributed to the development of shadow banking in the US, which in turn precipitates moral hazard and financial system instability. Regarding the role of FinTech development in influencing financial intermediation, Philippon [26] emphasizes on the disadvantages of FinTech in reducing intermediation cost. On the contrary, Fuster et al. [27] highlight the positive impact of FinTech platforms in lending expansions, particularly in spurring mortgage loans originating processes. It is shown that FinTech lenders are faster and more elastic in processing mortgage loan applications, especially when dealing with mortgage demand shocks. Tang [3] also finds that FinTech companies in the form of peer-to-peer lending platforms offer successful substitution for traditional banking services. Recent studies showing the negative impact of FinTech developments on the performance of the banking industry. Lv et al. [6] find that FinTech has a cooperative relationship with bank profitability, with a "U"-shaped impact on profitability, initially decreasing it but ultimately leading to increased profitability. On the other hand, Zhao et al. [7] show that financial technology innovation reduces banks' profitability and asset quality, particularly for large state-owned commercial banks, although it improves capital adequacy and management efficiency to some extent. Nguyen et al. [8] find that FinTech credit tends to reduce bank profitability while improving risk-related performance, suggesting that FinTech competes with banks and takes some profits but also benefits banks in terms of stability. However, stricter regulations can contribute positively to bank stability, and the impact of FinTech on bank performance may depend on the degree of banking regulation. Yudaruddin [28] demonstrates that FinTech startups have a detrimental effect on bank lending, while Yudaruddin [29] finds that FinTech startups also negatively impact bank performance. Another strand of literature highlights the impact of FinTech development on shareholder value creation. Chen et al. [30], emphasize that financial innovation indeed positively influence shareholder value. Meanwhile, Juengerkes [31] argues that collaborations between banks and FinTech firms may also increase customer trust. Likewise, Anagnostopoulos [32] highlights that FinTech development provides various benefits for the financial services industry through welfare outcomes for consumers, regulatory, and supervisory gains as well as reputational gains. Despite FinTech potentials, Li et al. [33] assess incumbent US retail banks and find that the roles of FinTech companies are limited to influence bank performance. In fact, prior literature on FinTech development do not differentiate types of FinTech firms. A large number of studies are also conducted in developed countries. Only Narayan and Sahminan [34] and Phan et al. [15] examine the impact of FinTech development in a developing country. Specifically, Narayan and Sahminan [34] investigate the impact of FinTech on exchange rate fluctuation and inflation in Indonesia from 1998 to 2017. It is shown that FinTech development can reduce inflation and appreciate Rupiah value against the US dollar. Phan et al. [15] investigate using a sample of 41 Indonesian banks and find that an increase in the number of FinTech companies reduces bank performance. However, as described erlier, they do not assess the issues of bank stability, while the implications of different types of FinTech firms on bank stability also remain unexplored. Indeed, Cheng and Qu [10] and Fung et al. [11] also assess the FinTech-financial stability nexus. Nevertheless, they do not assess specifically the influence of FinTech firms of different types on the stability of banks with certain characteristics. Cheng and Qu [10] develop a bank FinTech measure using web crawler technology and word frequency analysis, highlighting that the development of bank FinTech is associated with lower credit risk in Chinese banking. Meanwhile, using a sample of listed banks in 84 countries, Fung et al. [11] document mixed results on the link between FinTech development and financial stability in which FinTech development is measured using the presence of FinTech regulatory sandboxes as an exogenous shock to the banking industry. In this paper, our contribution is twofold. First, we assess the impact of FinTech on bank stability by differentiating types of FinTEch firms. We specifically investigate whether peer-to-peer lending (P2P) and payment system platforms affect bank stability. Building on the work of Phan et al. [15] who only focus on the link between P2P and bank performance, we are therefore the first to include payment system platforms in addition to P2P in analyzing their impact on bank riskiness. Second, we further investigate whether the interplay of FinTech and bank stability also depends on bank types. For this purpose, we differentiate banks into several categories. First, we differentiate between listed and non-listed banks, because listed banks are subject to market discipline that can be less suceptible to risk-taking behavior [35]. Second, we also distinguish Islamic banks from conventional banks, because no consensus has been found whether Islamic banks are more stable than conventional banks. Srairi [36] find that Islamic banks are indeed more stable than conventional banks, but some studies also report that Islamic banks are more suceptible to higher risk taking than conventional banks [[37], [38], [39]]. Third, we also consider banks of different size to take into account the notion that larger banks might be more suceptible to moral hazard and risk taking. From previous studies, large banks may undertake higher risk taking due to the “too big to fail” effects [[40], [41], [42], [43], [44]]. Given that large banks may exhibit higher incentive to undertake risk taking for “gamble for resurrections”, the presence of FinTech firms might affect larger banks to cooperate or compete with FinTech firms to undertake risky projects. In summary, previous literature on FinTech development has shown that FinTech firms can disrupt traditional banking services and provide new alternatives for customers. However, the impact of FinTech on bank stability and performance remains ambiguous, with mixed results on the relationship between FinTech development and financial stability. Furthermore, the potential risks associated with FinTech, such as its contribution to shadow banking and increased risk-taking behavior, have also been highlighted. This paper aims to contribute to the literature by examining the impact of different types of FinTech firms on bank stability, specifically peer-to-peer lending and payment system platforms. Additionally, we investigate whether the interplay of FinTech and bank stability depends on bank types, including listed vs. non-listed banks, Islamic vs. conventional banks, and banks of different sizes.

All annual bank-level indicators are retrieved from the Indonesian Financial Services Autority (Otoritas Jasa Keuangan/OJK) and we obtain an unbalanced panel dataset covering 141 commercial banks in Indonesia (i.e. conventional banks and Islamic banks) from 2004 to 2018. Market share data for each bank in 2018 is presented in Appendix 1 and 2 (A1 & A2). In the meantime, data on the number of FinTech firms from year to year are obtained from FinTech Singapore database (https://fintechnews.sg/) and The Indonesian Joint-Funding Fintech Association (Asosiasi Fintech Pendanaan Bersama Indonesia/AFPI). As reported in FinTech Singapore, there are several forms of FinTech in Indonesia. These include: (1) personal finance and wealth management; (2) payment; (3) point of sale systems; (4) lending; (5) accounting; (6) comparison; (7) insurtech; (8) crowdfunding; and (9) crypto and blockchain. However, this paper is focused on analyzing the role of FinTech lending and payment platforms in addition to assessing the role of total number of FinTech companies in influencing bank stability. This is because FinTech companies related to lending and payment services dominate 76% of the total number of FinTech companies in Indonesia up to 2018. Meanwhile, both types of FinTech firms tend to have comparable business models with banking as financial intermediary in the payment system and credit markets. Hence, bank stability is more likely affected by FinTech lending and payment companies, as an increase the number of both FinTech firms can intensify competition in the banking industry.1

Following Lepetit and Strobel [45], Yusgiantoro et al. [44], Maria et al. [46], Yudaruddin [47], we use bank insolvency risk and capital ratios as dependent variables. Insolvency risk for bank i at year t are represented by Z-score measures based on the following formula.where MROA is the bank i's average return on assets from 2004 to 2018, while SDROA is the standard deviation of the bank's return on assets during the 2004–2018 period. Higher ZEQTA and ZCAR are associated with lower bank insolvency risk and stronger bank stability, because banks with higher Z-scores are able to increase enough capitalization to offset higher income volatility. Meanwile, capital ratios for bank i at year t are measured using EQTA and CAR. EQTA is the ratio of total equity to total assets, while CAR is the ratio of total capital to risk weighted assets. Banks with higher capital ratios also tend to exhibit stronger financial position and lower riskiness.

In terms of explatory variables of interest, we use FINALL measured by the total number of FinTech firms in all categories for each year. Meanwhile, we use P2P and PAY to reflect the number of FinTech lending and payment platforms for each year, respectively. The measurement of these indicators is also used by previous studies, although the roles of FinTech lending and payment platforms remains unexplored (e.g. Refs. [15,18,34]). Moreover, we also include several bank-specific variables as control variables that affect bank stability. First, we incorporate the role of bank concentration measured by the share of the five largest banks' total assets over the Indonesian banking industry (CR5). However, the link between bank concentration and financial stability remains inconclusive. On the one hand, the concentration-stability hypothesis suggests that banks in less competitive markets are more susceptible to experience higher riskiness due to a decline in bank market power (e.g. Refs. [44,[48], [49], [50], [51], [52]]). On the other hand, the competition-stability hypothesis emphasizes that banks in more competitive markets tend to exhibit lower riskiness due to a decline in bank borrowers’ moral hazard in the credit market (e.g. Refs. [[53], [54], [55], [56]]). Second, the logarithm of total assets bank (SIZE) is also included as a control variable to take bank size into account. Banks with larger size can be more stable than small banks because larger banks can diversify their income [40,57]. Conversely, smaller banks may also exhibit lower riskiness than larger banks due to lower moral hazard (e.g. Refs. [[41], [42], [43]]). Third, DEPO is also incorporated as a control variable to control for bank liquidity. DEPO is measured by the ratio of total third party funds to total assets. Higher bank liquidity can be detrimental for financial stability, because banks with higher liquidity tend to finance risky projects in the asset side (e.g. Refs. [[58], [59], [60]]). However, higher liquidity can also be associated with lower riskiness due to bank prudent behavior [61]. Fourth, we also control for the role of financial intermediation measured by the ratio of total loans to total assets (LTA). Higher credit growth may be associated with higher bank riskiness due to a decline in credit and collateral standards (e.g. Refs. [62,63]). Fifth, we control for bank inefficiency measured by the ratio of operating expenses to operating income (CTI). Berger and DeYoung [64], Altunbas et al. [65], and Fiordelisi et al. [66] suggest that inefficient banks seem to take on more risk. On the contrary, more inefficient banks can also be more stable at least in the ASEAN context [67]. In addition, NON measured by the ratio of non-interest income to total assets is also incorporated as an independent variable to control for the role of bank income diversification. Altunbas et al. [68] and Demirgüc-Kunt and Huizinga [42] find that higher non-interest income tend to strengthen bank stability, particulary in small banks. Finally, we include two dummy variables (IB and CRISIS) to assess the role of Islamic bank principles and crisis period in affecting bank stability, respectively. IB equals 1 if a bank is clasified as an Islamic bank, while CRISIS equals 1 for the 2008–2009 period reflecting the onset of the global financial crisis. Indeed, Islamic and conventional bank behavior in risk taking remains unclear ([[36], [37], [38], [39],69,70]). Meanwhile, Soedarmono et al. [71] shows that bank behavior may be different between normal and crisis periods. Yet, Alqahtani & Mayes, [72]. focus on the Gulf Cooperation Council region and find that finding Islamic bank stability is altered after the global financial crisis.

To investigate the link between FinTech and bank stability, we proceed the analysis in several stages. First, we initially investigate the impact of FinTech on bank stability in general. Second, we assess whether the stability of Islamic banks and conventional banks differs following an increse in the number of FinTech companies. Third, we analyze the risk-taking behavior of large banks and small banks due to an increase in the number of FinTech companies. Finally, we also analyze whether listed banks and non-listed banks exhibit different risk-taking behavior following FinTech development. In the first stage, we estimate Eq. (1) in order to test whether FINALL, P2P and PAY affect bank stability in general. From Eq. (1), FS represents one of the bank stability variables used in this study (ZEQTA, ZCAR, EQTA, or CAR), while FINTECH represents one of the explanatory variables of interest (FINALL, P2P or PAY). In the second stage, we assess whether Islamic banks and conventional banks behave differently in terms of risk taking following an increase in the number of FinTech companies. Because the number of Islamic banks in Indonesia is limited (13 Islamic banks) compared to the total number of conventional banks in our sample, we tackle this issue using an interaction variable between Islamic bank dummy and FinTech indicators as in Eq. (2) instead of differentiating bank sample into Islamic banks and conventional banks.In the third stage, we repeat the estimation conducted in Eq. (1) for two different sub-groups of banks (large banks and small banks) according to the size of core capital. That is, we consider banks in BUKU 4 and BUKU 3 as large banks, while banks in BUKU 1 and BUKU 2 as small banks.2 In the fourth stage, we conduct again the estmation for Eq. (1), but we now differentiate bank sample into listed banks and non-listed banks. According to Hadad et al. [35], listed banks are subject to greater market discipline than non-listed banks. In this regard, the effect of FinTech development on bank risk taking might be different for banks with different influences of market discipline. To estimate these four stages, we use a dynamic panel data model with the two-step system GMM (generalized methods of moments) estimator developed by Blundell and Bond [73] and Arellano and Bover [74]. Recent studies in FinTech also use a dynamic panel data analysis [12,27,30,33]. In general, the use of a dynamic panel data estimation in studying the determinants of bank stability is relevant, because bank risk as a dependent variable might be affected by its past values. Using a dynamic panel data estimation can also avoid reverse causality problems between independent and dependent variables. Moreover, we opt to use the two-step system GMM estimator in our dynamic panel data models, which enables us to obtain unbiased coefficients, particularly when the movement of dependent variables are close to a random walk. Moreover, robust standard errors and small sample correction following Windmeijer [75] are also taken into account. Finally, we conduct orthogonal deviations of instruments to deal with plaussible bank-level fixed effects. Overall, the two-step system GMM estimation is valid when the AR(2) test and the Hansen-J test are both not statistically signfiicant.

In this section, we present the summary statistics of our variables reported in Tabel 1. The average of ZEQTA, ZCAR, EQTA and CAR in our sample are 18.92, 31.55, 0.14 and 27.64, respectively. The standard deviation of ZEQTA, ZCAR, EQTA and CAR in our sample are 15.72, 28.02, 0.10 and 26.3, respectively. Overall, the mean values of all the variables are smaller than the standard deviation, showing that our bank risk measures are not highly volatile. We also report the correlation of all variables in Table 1, Table 2 in which no strong correlation can be detected among independent varables and hence, multicollinearity issues are less of a concern. Although FINALL, P2P and PAY are strongly correlated, we do not include these variables in one regression model.

Table 3 provides baseline regressions to assess the link between FinTech and bank stability. Regardless of the measures of bank stability (ZEQTA, ZCAR, EQTA or CAR) and FinTech (FINALL, P2P, or PAY), we document that more FinTech firms are associated with lower bank riskiness and hence, stronger financial stability in general. All dynamic panel data models in Table 3 are valid, because the AR(2) test and the Hansen-J test are not rejected. These findings are supportive of the notion that FinTech development is beneficial for banking (e.g. Refs. [30,32,33]). Our findings are different from Phan et al. [15] who emphasize on the adverse impact of FinTech development on bank performance in Indonesia. Despite the positive impact of FinTech on bank stability, our findings might also indicate that FinTech businesses are not mature yet, because only mature FinTech firms are able to disrupt incumbents in the financial systems, including banking [21]. Consequently, FinTech development does not impair bank stability. Likewise, our findings can advocate the importance of FinTech development in strengthening bank stability. Regarding the role of control variables, we document that bank concentration has a significant and negative impact on bank stability. This finding is in line with the competition-stability hypothesis (e.g. Refs. [[53], [54], [55]]). Meanwhile, bank size (SIZE) is not significant in explaining bank insolvency risk, but it is negatively associated with bank capital ratios. This finding confirms the notion that large banks tend to hold lower capitalization (e.g. Refs. [[41], [42], [43]]). In the next turn, bank deposits (DEPO) do not have a statistically significant impact on bank insolvency risk. However, bank deposits exhibit a negative and significant impact on bank capital ratios. Yet, the loan-to-assets ratio (LTA) has a significant and negative impact on bank stability measured by ZCAR, EQTA or CAR. This finding supports previous literature reporting that bank loan growth adversely affects financial stability [59,62,63]. We also find that CTI exhibits a positive impact on bank stability measured by bank solvency or capital ratios as in Mongid et al. [67]. The Islamic bank dummy (IB) exhibits a negative and significant impact on bank stability indicators (ZCAR, EQTA or CAR) and hence, conventional banks tend to be more stable than Islamic banks following Čihák and Hesse [37] and Ajmi et al., [38]. We also find that the Indonesian banking industry exhibits stronger financial stability and holds more capital during the 2008–2009 global financial crisis. This is consistent with Berger & Bouwman [76] in which banks are more prudent in times of crises.

Although FinTech development is positively associated with bank stability in general, some studies point out that risk-taking behavior might differ according to bank types. Hence, the influence of FinTech development on bank stability might also be conditional on bank types. Previous studies highlight that Islamic banks and conventional banks exhibit different riskiness, because Islamic banks are subject to greater information asymmetry problems between banks and borrowers [77]. Hence, we assess whether Islamic banks and conventional bank riskiness changes due to FinTech development (FINALL, P2P or PAY). Moreover, small banks and large banks might also be different in risk taking due to the “too big to fail” effects (e.g. Ref. [44]). Accordingly, we also assess whether the impact of FinTech development on bank stability is dependent on bank size. Finally, we investigate whether listed banks and non-listed banks also behave differently in risk taking due to FinTech development, given the fact that listed banks are subject to greater market discipline [35]. Table 4 presents our empirical results to assess whether Islamic banks and conventional banks pursue different risk-taking strategies in facing an increase in the number of FinTech firms. We document that the coefficients of the interaction terms between FinTech variables (FINALL, P2P or PAY) and IB are indeed negative and significant. However, their negative values are still lower than the positive values of coefficients of FINALL, P2P or PAY as standalone variables. This means that the role of FinTech development in strengthening bank stability is not different between conventional banks and Islamic banks. In Table 5, we assess whether FinTech development affects insolvency risk in large banks and small banks. It is shown that the positive impact of FinTech development measured by FINALL, P2P or PAY on bank stability is more pronounced for small banks. This suggests that small banks benefits more from the presence of FinTech firms, particularly online peer-to-peer lending and payment service platforms. Moreover, Table 6 documents identical results when we use capital ratios (EQTA or CAR) as dependent variables. In other words, the positive link between FinTech development and bank capital ratios is also more pronounced for small banks. Eventually, Table 7 and Table 8 provide empirical evidence on the link between FinTech development and bank stability for listed banks and non-listed banks, respectively. Our findings highlight that the positive impact of FinTech on bank stability and capital ratios is more pronounced for non-listed banks. In the next turn, we describe some robustness checks to ensure that the interplay of FinTech and bank stability, taking into account bank-specific characteristics, is not altered. However, the results of robustness checks are not presented in this paper, but are available upon request. First, building on the robustness check undertaken by Phan et al. [15], we consider FinTech variables with a one-year lag. Overall, our main results remain consistent in which higher number of FinTech firms including online P2P lending and payment platforms is associated with bank stability one year ahead. In addition, the positive impact of FinTech firms on bank stability is also more pronounced for small banks and non-listed banks. Second, we run regressions using an alternative panel data estimator. Following Demirgüç-Kunt and Huizinga [42], González et al. [39], and Phan et al. [15], we conduct regressions using a two-way panel fixed effect model. In general, our results presented earlier from Table 3 to Table 8 are not altered.

In this paper, we assess the link between FinTech firms and bank stability, taking into account different types of FinTech and banks. Using a sample of 141 commercial banks in Indonesia during the 2004–2018 period, our main findings indicate that more FinTech companies in general, as well as more online P2P lending and payment service firms, tend to enhance bank stability. Moreover, a closer investigation shows that these findings are more pronounced for small banks and non-listed banks. Our findings are robust to some econometric specifications. Eventually, our present paper might provide some policy implications. For small banks or non-listed banks, we advocate the importance of collaborations betweeen FinTech firms and banks in mitigating bank riskiness. In other words, incorporating FinTech business models into these banks, or enabling cooperations with FinTech firms are essential for financial stability in small banks and non-listed banks. For large banks and listed banks, strengthening regulatory frameworks to enable prudent collaborations with FinTech companies remains necessary to ensure that more FinTech firms do not intensify unhealthy competition that may precipitate risk-taking behavior of large banks and listed banks.

In general, the findings of this study suggest that FinTech development can have a positive impact on bank stability, which may have important implications for policymakers and investors. From a policymaker perspective, these results highlight the importance of promoting and supporting the growth of FinTech businesses, as they can contribute to a more stable financial system. Policymakers can consider implementing policies that facilitate the entry of FinTech firms into the market, while also ensuring that the regulatory framework remains appropriate to mitigate potential risks associated with their activities. From an investor perspective, the results suggest that investing in FinTech firms may be a promising strategy for generating financial returns while also contributing to the stability of the financial system. Investors can consider allocating a portion of their portfolio to FinTech firms, particularly those that have shown to be more mature and capable of disrupting incumbents in the financial system. Given the large sample size used in this study, the findings have the potential to inform the entire banking system in Indonesia. Policymakers and investors can use these results to inform their decisions and strategies, and to promote a more stable and resilient financial system in the country. However, there are several limitations to our study that should be considered in future research. First, our study only focuses on the Indonesian banking sector, which limits the generalizability of our findings to other countries and regions. Second, our study only considers the impact of FinTech firms on bank stability, without considering the potential risks associated with their activities. Future research could investigate the potential risks associated with FinTech firms, and how they can be mitigated. Lastly, our study only considers a specific time period and may not capture the long-term effects of FinTech development on bank stability. Future studies could extend the sample period to investigate the long-term effects of FinTech development on the banking sector, for example during the COVID-19 period.

Rizky Yudaruddin; Wahyoe Soedarmono; BRAMANTYO ADI NUGROHO; ZHIKRY FITRIAN; MARDIANY MARDIANY; ADI HENDRO PURNOMO; EKA NOR SANTI: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Data will be made available on request.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.