Nexus Between Fintech Innovations and Liquidity Risk in GCC Banks: The Moderating Role of Bank Size

Abstract

Fintech is a modern phenomenon that is transforming the banking industry through innovations that streamline financial processes and improve efficiency. The increasing adoption of disruptive technologies prompts inquiries regarding their potential to either bolster banks’ stability or expose them to various challenges and risks, including liquidity issues. Hence, this paper analyzes the effect of fintech innovations on liquidity risks in commercial banks across the six GCC countries (comprising a major financial market) during the period from 2018 to 2023. To develop a panel data methodology, we chose a sample of 26 commercial banks. The findings from our analysis indicated that (1) fintech innovations have a negative relationship with liquidity risks and (2) the size of the bank moderates the connection between fintech and liquidity risks (whereby larger banks significantly affect the relationship between fintech innovations and liquidity risks).

1. Introduction

The global economy is currently undergoing a significant transformation in terms of rapid digital technology and innovation adoption, marked by rapid advancements in artificial intelligence (AI), blockchain, cloud computing, and other tools that are generating unprecedented efficiencies and productivity in the financial sector, including the rapid development of financial technology (fintech) (Goralski & Tan, 2020). As we increasingly depend on various fintech innovations (FIs) that are reshaping the worldwide economy, it becomes clear that the financial industry must adapt by investing in technology, especially in this fast-evolving digital era (Gabor & Brooks, 2020). FIs cut across various activities within the financial and technology industries, and their influences on the economy differ in scope and significance (Barman et al., 2022). Between 2017 and 2021, there were 19,400 patents granted globally in the realm of fintech, submitted by over 7000 fintech companies. This information, released by the World Intellectual Property Organization (Fang et al., 2023), highlights the rapid proliferation of fintech products, but in itself this says little regarding the essential role that innovations in fintech might play in strengthening the financial sector’s capabilities and, importantly, in reducing financial risks and instilling confidence in the financial industry’s products and activities.

Amid the major economic disruption associated with the COVID-19 pandemic, the wheel of development and innovation in the field of fintech continued rapidly, which has had major impacts on the banking sector worldwide (Corbet et al., 2020). Public health “lockdown” measures from 2020 onwards galvanized existent efforts by banks and their customers to shift from traditional models to innovative new products and services, such as digital wallets, contactless payments, and AI-powered customer service, in addition to changing the competitive environment (Murinde et al., 2022). In response to the economic disruption caused by lockdowns, organizations needed to repay debts, reallocate capital, and find the necessary financing by providing liquidity from banks, which required electronic financial services (Saif-Alyousfi, 2025). Consequently, banks increasingly migrated from traditional systems to adopt recent technological innovations in financial service operations and delivery.

Considering that banks are the main pillar of financial systems and macroeconomic activities worldwide, due to diffusing capital into economic systems, financial intermediation theory illustrates the vital and fundamental role of the banking sector in reallocating and distributing capital, as it is the primary provider of indirect financing, by providing necessary liquidity in markets. Liquidity management is an important process for maintaining the financial stability of any economy, and this is carried out by balancing between demand and supply for liquidity in markets (Murinde et al., 2022). Following the outbreak of COVID-19, global markets and business environments suffered from volatility and fluctuation, which required banks to manage their liquidity more efficiently in light of these rapid changes in technology and the extreme adverse economic impacts of lockdowns (e.g., disruptions to manufacturing, supply chains and logistics, and consumerism), including a drastic collapse in foreign direct investment (FDI) in MENA countries (Saif-Alyousfi, 2025).

FIs have played a significant and vital role in the banking industry for some years, but they also pose new kinds of risks (Lv & Xiong, 2022). They can lead to cyber breaches, operational risks, and additional costs. On a different note, fintech companies are reshaping the competitive landscape by offering loans directly to customers, posing a threat to the traditional lending business of banks, and shifting deposits in their favor. On the flip side, FIs have the potential to significantly enhance transaction efficiency and introduce modern business models based on advanced technology tools. They also have the power to boost the role of management in banking operations, fostering a more competitive landscape and attracting customers to create the necessary liquidity (Wu et al., 2024).

Given the increasing importance of fintech, the existing literature remains limited, particularly in the context of the Middle East and North Africa (MENA) region, and more specifically the Gulf Cooperation Council (GCC), where financial systems are rapidly evolving. Most previous studies have focused on developed economies or broader global samples, often overlooking the unique financial and regulatory structures of GCC countries.

This study contributes to the literature in several novel ways. First, it is among the first to empirically examine the relationship between fintech innovations and liquidity risk in GCC commercial banks, filling a geographic and contextual gap in the existing body of research. Second, unlike prior studies that often treat fintech as a single proxy or use simple adoption measures, we construct a composite fintech index grounded in the Basel Committee’s indicators, tailored to reflect the actual nature of banking innovation in the region. Third, the study introduces bank size as a moderating variable, a methodological advancement that captures how fintech impacts may differ across small and large institutions—an angle largely neglected in prior work. Finally, by using FEM AND GMM, the study captures their effects, providing a richer understanding of the fintech–liquidity nexus. These contributions collectively offer a more contextualized and dynamic analysis, advancing both theory and practice in the field of fintech and banking risk management.

Following this introductory section, which provides the background context of the subjects of this study, the next section reviews and discusses related studies, followed by the presentation of the research methodology and data collection methods used. The empirical results are then presented, followed by the discussion and conclusion of this study’s outcomes.

2. Literature Review

2.1. Financial Technology Innovations (FIs)

“Fintech” is a relatively amorphous concept that pertains to the combination of banking operations and modern technology (e.g., blockchain and AI). It is in a continuous process of development and modernization that has led to the emergence of many novel financing tools and options, as well as improvements to traditional financial services and functions. The rapid and continuous development of fintech itself led directly to the emergence of new technologies like blockchain, big data analytics, AI-related technologies, cloud computing, smart contracts, and cryptocurrencies (Kyriazis et al., 2020). Such solutions are rapidly spreading across various sectors of the financial industry, such as crowdfunding, insurance, finance, wealth management, and third-party payments (Kyriazis et al., 2023). These technologies have reshaped and reorganized the banking industry and have affected the ways in which services are delivered to customers (Kyriazis et al., 2023).

Blockchain technology plays a major role in improving the security of online information exchange, in addition to data security, transaction reliability, and reducing related costs (Notheisen et al., 2017). While big data enhances data processing and integration efficiency, enabling personalized services and products to consumers (Anshari et al., 2019), smart contracts contribute to enhancing transparency and reducing costs, whether related to transactions or legal concerns, additionally contributing to the automation of processes, which increases the efficiency of financial institutions (Corbet et al., 2022). Electronic payments provide innovative solutions to meet customer needs, especially in online transactions (Gomber et al., 2017).

Therefore, it can be said that fintech works to create new spending behaviors for individuals and innovative ways to access financial services, in addition to redistributing wealth. This technology has had a significant impact on transactions, as it has helped reduce their costs, expand feasibility, enhance the risk management process, improve pricing efficiency, and reduce information asymmetry, and the development of fintech can consequently be expected to exert profound and long-lasting impacts on the financial system as a whole (Allen et al., 2021; Anagnostopoulos, 2018; Y. Wang et al., 2021).

2.2. FIs and the Banking Industry

The banking industry has always embraced and pioneered modern technologies in its activities and financial transactions. In the context of fintech, it has revolutionized the banking and financial sector and provides a banking platform to facilitate transfers through electronic payment networks (Thompson, 2017). Fintech helps expand the services banks provide to customers rather than merely facilitating traditional services with technological augmentation. Fintech applications are characterized by a high level of accuracy and security in facilitating access to traditional services provided by banks and potentially offer new ones (Halimi et al., 2022).

From the consumer perspective, fintech offers new experiences in conducting and completing banking and financial transactions in a more convenient and sophisticated manner, saving time and effort. Through tablets and mobile phones, customers are able to use the services provided by banks anywhere instead of going to high street banks, which in turn brings many benefits to banks and customers alike (Fuster et al., 2019). However, fintech can exacerbate exclusion for those unable to access modern digital technologies. Indeed, in some contexts, fintech has been associated with an expanded “financial inclusion gap”, whereby public–private partnerships to develop digital platforms and support smartphone payment use have been hampered by “illiteracy, poor infrastructural facilities, intermittent power supply, poor mobile receptions… banks’ network failures, unnecessary charges, information asymmetry and data privacy breaches” (Ediagbonya & Tioluwani, 2023).

Studies have drawn attention to the connection between fintech and traditional banking, highlighting the direct influence exerted by fintech as one of the major determinants of banking sector development, with FIs offering potential or transformative changes in bank performance and exerting profound influences on the traditional banking industry (Dwivedi et al., 2021; Zhao et al., 2022). Al-Dmour et al. (2021). This suggests that big data in particular can be leveraged to massively improve bank performance. However, the growth of fintech is a source of risk in addition to opportunities for banks.

R. Wang et al. (2021) cautioned that fintech growth has exhibited negative impacts on aspects of banking and has subsequently increased the risks faced by banks in general. For instance, the high initial costs required to invest in modern financial technologies and the long period required for the results of technology implementation to appear—in other words, to accrue return on investment (ROI)—can expose banks to significant financial pressures, leading to increased risks. Moreover, the expanding number of fintech companies and the advent of challenger banks given a fillip by fintech solutions has increased competition within the financial sector, contributing to the decline in traditional banks’ market share and lower profits, thus increasing their exposure to risks.

Conversely, Deng et al. (2021) indicate that banks’ investment in fintech reduces administrative costs, raises the operational efficiency of banking transactions, and reduces the risks that the banking sector may face (in general). In this context, Banna et al. (2021) indicated similar results in Organization of Islamic Cooperation (OIC) countries. The adoption of modern fintech can help improve banks’ ability to manage the risks they are exposed to, as Al-Shouha et al. (2024) and Cheng and Qu (2020) indicated, whereby banks’ investment in fintech contributes to reducing the credit risks to which these banks are exposed.

Adopting FIs in the banking sector has improved the performance of banks (Al-Shouha et al., 2024). Investing in fintech infrastructure allows banks to expand their customer base and plays a crucial role in significantly reducing credit risk, providing a sense of security to both banks and their customers. This, in turn, saves operating costs. Therefore, banks should combine resources to facilitate digital technology and financial innovations.

In general, the previous literature has presented investigations of the impact of fintech on banks’ performance, efficiency, and the risks to which they are exposed. Still, the relationship between the development of fintech in banks and liquidity risks could have been more robust, especially in the GCC. Therefore, the current study investigates this issue using evidence from the Jordanian market.

2.3. FIs and Liquidity Risk

The rapid pace of FIs and their increasing applications in the banking sector can potentially revolutionize bank liquidity (Murinde et al., 2022). While such innovations may initially lead to a decrease in bank liquidity by reducing profitability and increasing risks, it is important to note the potential for growth and development (Li et al., 2022). Banks’ investments in fintech, despite the high establishment and maintenance costs, can lead to a reduction in short-term profits, but also to the creation of new opportunities and claims (R. Wang et al., 2021).

Many theories have addressed the relationship between technological innovations and liquidity in banks. Disruptive innovation theory suggests that innovations in fintech can lead to radical transformations in markets (Gomber et al., 2017). Innovations have led to changes in how customers interact with financial services. For instance, as the usage of financial apps grows, clients may choose these services over conventional accounts, affecting the flow of money to banks (Gomber et al., 2017). In this context, financial innovations generate new market rivalry by providing more flexible and expedited services, thereby luring clients from traditional banks and affecting their capacity to sustain liquidity levels (Jameaba, 2020).

On the other hand, consumer theory assumes that consumers make decisions based on their preferences and expectations; they tend to look for the most cost-effective options (Frederiks et al., 2015). Innovations, such as online loans with lower interest rates than those offered by traditional banks, attract consumers, which affects banks’ liquidity (Broby, 2021). In the context of the “fragility channel”, FIs increase new risks and pressures on the banking system, such as possible cyberattacks (Jameaba, 2020). A security breach may cause customers to lose trust in banks, prompting them to withdraw their funds and thereby reducing liquidity. In addition, technological innovations require the adaptation of legal and regulatory systems (Lescrauwaet et al., 2022). The lack of clarity in policies in this milieu can lead to market instability, affecting confidence and contributing to increased financial fragility.

Technological growth in banks increases debt costs and risks (Qiu et al., 2018). At the same time, Deng et al. (2021) indicated that banks facing a highly competitive environment may increase deposit rates to stimulate deposit business, increasing capital pressure on banks. In the same context, Zhao et al. (2022) confirmed that competition between banks resulting from innovations and developments in fintech leads to a decrease in the quality of banks’ assets and a deterioration in their profitability. Fintech and its innovations create new and innovative banking services and products that have the potential to replace traditional services in the banking sector (Kyriazis et al., 2023), thereby transforming the industry and creating an atmosphere of competition between banks.

As previously noted, banks’ costs associated with using and investing in fintech can increase, reducing their short-term profitability, ultimately eroding their capital, and reducing their ability to withstand financial shocks (Stulz, 2019). This may weaken banks’ ability to provide necessary liquidity. FIs, such as AI and big data, may increase the risks to which banks are exposed, which can increase operational risks in the banking industry (Kharrat et al., 2024).

In the era of FI, banks face more and more security risks pertaining to data protection and app security (Jović & Nikolić, 2022). On the other hand, taking the road of fintech does mean that faster as well as cheaper solutions are now available to access, and therefore some people could find it an attractive alternative line of service (Van Loo, 2018). As a result, the customer base of banks may change, quite probably leading to liquidity risk (Tang et al., 2024). As a result, we can say that the two risks are operating or credit, which ultimately will result in reduced liquidity for banks.

The competition and innovation of the fintech companies have an intense impact on traditional banks (Yang & Wang, 2022), which will seriously harm their traditional operations and thus bring financial liquidity problems. Boot et al. (2021) interpreted that fintech companies may also redefine the role of banks as middlemen in credit, the processing of deposits, and their use on a very large scale. This will change what banks can do. More importantly, it also threatens the lending business.

In contrast, fintech is able to improve banks’ liquidity by enhancing their business processes and customer base, which increases banks’ deposit rates and volume of loan contracts (Belanche et al., 2019). Traditional banks face difficulties in understanding changes in customer needs and updating their products due to their legacy systems, and they often lose customers as a result. FIs help banks overcome these problems by developing their own operations, improving customer experience, and creating channels of communication to keep long-lasting customer relationships between branches, so that bank transactions grow, thereby raising liquidity, ultimately, for all concerned (Semwayo, 2024).

Fintech is not poised to replace traditional banks per se, but to open avenues through which all banks can offer ancillary services, which can be incorporated as an important cooperative partner for banks in the process of handling credit risks to increase liquidity (Odinet, 2020). In addition, banks can use big data technology to reduce information asymmetry between banks and clients, thereby reducing the risk of loan defaults (Y. Wang & Wen, 2024).

Innovations in the banking sector spark market competition, which pushes banks to borrow and store money (Ding et al., 2022). Competition in this field promotes the development of banking legislation and the expansion of regulatory policies, providing more money out of the organization of loans, so that they receive more funds. Consequently, banks’ liquidity becomes better. Based on this, we put forward the following hypothesis:

H1: 

FIs affect liquidity risk in GCC banks.

2.4. The Moderating Role of Bank Size

Banks’ sizes, as determined by their assets, deposits, and loans, are a crucial factor that significantly influences their operations and the stability of the financial system (Abisola, 2022). Banks’ sizes affect their ability to meet demands by providing necessary resources for operations and adapting to changes in the economy through the ability to obtain funding from a variety of sources (Diamond & Rajan, 2001). Compared to smaller banks, larger banks can access a wider range of funding sources at lower prices, which increases their ability to provide liquidity and lessens their reliance on short-term loans that could increase risks during emergencies (Acharya & Mora, 2015). Put simply, larger banks attain “economies of scale” (Varian, 2014). Large banks, according to Berger and Bouwman (2009), can reduce fixed costs more effectively because of their massive operations, which boosts their liquidity generation potential. Large banks can increase capital efficiency to create liquidity by expanding their customer base and providing a range of funding options.

Large banks can adopt technological innovations more quickly and completely than smaller banks, since they usually have greater financial resources and improved investment capacities. This has the potential to reduce transaction costs and increase operational efficiency, thereby improving profitability (Acharya & Mora, 2015). The agency theory clarifies the relationship between owners (principals) and managers (agents), emphasizing how conflicts of interest can lead to internal problems within banks (Jensen & Meckling, 1976). Large banks may increase the complexity of their internal structure by implementing fintech (Philippon, 2016), which would raise agency costs. To stay ahead of the competition or satisfy customer needs, managers may invest in costly technologies; this can have an impact on liquidity and lead to unexpected costs for banks (Buchak et al., 2018).

Major banks may have sophisticated risk and liquidity management systems that were developed through the allocation of resources to construct fintech infrastructures. Big banks may use AI and big data technologies to assess risks and manage loans more effectively, increasing their liquidity (Brunnermeier & Oehmke, 2013). Conversely, smaller banks are more susceptible to fluctuations in liquidity because they might not be able to devote resources to risk management as efficiently (Cornett et al., 2011). Resource-based theory suggests that larger banks possess superior resources relative to smaller banks, allowing them to implement fintech enhancements more successfully (Arner et al., 2015, 2017; Barney, 2000). However, these technologies need a significant allocation of banks’ resources, making them more susceptible to heightened limitations. Investing in FIs may jeopardize liquidity, since the capital requirements for implementing new technologies increase, leading to diminished liquidity levels (Tang et al., 2024).

Otherwise, it might be argued that large banks have easier access to domestic and global financial markets, which allows them to grow their networks, implement digital payment methods, and improve their financial operations (Kharroubi, 2015). This improves their ability to attract deposits and increase liquidity more efficiently than smaller banks. Conversely, the significant reliance of large banks on fintech can considerably complicate their operations. These banks may face challenges in integrating new technologies with their existing infrastructure, leading to increased operational risks that could affect liquidity (Cumming & Schwienbacher, 2021). For example, if systemic issues lead to operational delays or disruptions, major banks may be required to deploy liquidity buffers to alleviate these risks.

Transaction cost theory posits that increasing reliance on FIs leads to heightened operational costs for banks, especially large institutions that oversee a wide range of complex financial operations. These expenditures may increase reliance on cash for financial commitments. Although large banks have an intrinsically greater capacity to provide liquidity, they may face challenges due to the vast scope and complexity of their operations. According to Acharya and Mora (2015), major banks may encounter liquidity challenges if they heavily depend on unstable funding sources like short-term loans. Disruptions in financial markets may impede banks’ ability to provide liquidity due to the increased volume of loans.

Although the size of the bank has a significant impact on its effect, fintech can increase banking efficiency and liquidity. Major banks, for instance, can allocate substantial resources to cutting-edge fintech, enhancing their liquidity management via contemporary payment systems and asset management. Tang et al. (2024) looked at how bank size affects the usefulness of fintech. They found that bigger banks benefit more from technological advances that make liquidity better than smaller banks. This is because they can afford the costs of fintech and find more uses for it. Fintech breakthroughs necessitate substantial resources. Major banks that engage significantly in new technologies may encounter liquidity challenges due to the necessity of allocating substantial capital for digital transformation initiatives (Frost, 2020). Therefore, it can be hypothesized that

H2: 

Bank size moderates the relationship between FIs and liquidity management in GCC banks.

3. Materials and Methods

This paper examines the connection between FIs and liquidity risk using bank data collected from annual reports, financial reports, and Basel III disclosure reports for commercial banks operating in the six member states of the GCC (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the UAE). The economic variables were sourced from World Bank bulletins. The study period was set from 2018 to 2023, and the final study sample consisted of 26 commercial banks.

3.1. Independent Variable: Fintech Innovations (FIs)

This study constructs a Fintech Innovation Index using a text mining methodology, drawing on annual reports of GCC commercial banks. This method is inspired by recent empirical studies (e.g., Tang et al., 2024; Kharrat et al., 2024) that advocate for the use of qualitative disclosures as proxies for innovation engagement. Text mining offers a non-invasive, scalable, and replicable approach to capture how banks publicly frame their innovation strategies. It is particularly valuable in the GCC context, where consistent quantitative fintech data (e.g., R&D expenditure, fintech product counts) is often scarce or unavailable across all banks.

Ten keywords were selected based on the Basel Committee’s fintech framework and previous literature, including terms “Blockchain” “AI” “Internet” “Financial technology” “Online” “Mobile” “Digital” “Network” “Technology” and “E-payment.” These keywords were not arbitrarily chosen; rather, they represent dimensions proposed by the Basel Committee for financial technology (Tang et al., 2024) and were frequently present in the banks’ reports in MENA (Kharrat et al., 2024). The software AntConc 4 was employed to ensure systematic extraction of frequency data, providing an objective basis for index construction.

To address potential concerns about the gap between discourse and actual implementation, we acknowledge that textual mentions may reflect strategic signaling rather than operational depth. However, such discourse remains valuable; prior literature in corporate finance and accounting has shown that textual disclosures are strong predictors of firms’ strategic priorities and market orientation. In future work, this index could be triangulated with alternative fintech proxies, such as investment data, number of digital services offered, or partnerships with fintech firms, to further validate its robustness.

In contrast to other potential measures—such as survey-based indices or central bank fintech registries—this method allows for consistent cross-bank comparison using publicly available data and reflects how banks themselves articulate their innovation engagement, which is particularly relevant in examining market signaling and strategic positioning.

3.2. Dependent Variable: Liquidity Management

To measure the bank liquidity risk indicator, the net stable funding ratio (NSFR) international regulatory standard was used, which reflects the proportion of long-term illiquid assets funded by long-term liabilities or considered stable (such as core deposits). The proposed regulations by Basel III require maintaining an NSFR above 1 and disclosing it (Vazquez & Federico, 2015). It is one of the quantitative regulatory standards set by the Basel Committee to assess bank liquidity, whereby Basel III obligated banks to maintain sufficient and stable funding to cover the stable funding requirements, which enhances flexibility in the long term (Tang et al., 2024). It is defined as the ratio of available stable funding to required stable funding, as shown in Equation (1). To calculate NSFR, Basel III approved the classification and weighting of bank assets and liabilities; Appendix A (

Table A1. Liquidity classification of bank activities (NSFR).

Activity/Asset	Classification	ASF/RSF Contribution
Capital instruments	Long-term stable funding	100% ASF (Tier 1 and Tier 2 capital components)
Retail deposits (stable)	Core deposits	95% ASF (with insurance)
Retail deposits (less stable)	Other retail deposits	90% ASF (without deposit insurance or less established)
Wholesale funding (long-term)	Committed stable funding	50–100% ASF (depending on contractual maturity)
Wholesale funding (short-term)	Volatile funding	0–50% ASF (depending on relationship and maturity)
Loans to non-financial corporates	Illiquid assets	85% RSF (loans maturing > 1 year)
Residential mortgages	Illiquid assets	65% RSF (non-securitized mortgages maturing > 1 year)
Short-term loans (corporates)	Relatively liquid assets	10% RSF (short-term loans < 1 year)
High-quality liquid assets (HQLAs)	Liquid buffer	0–5% RSF (depending on HQLA level 1, 2A, or 2B)
Other marketable securities	Relatively liquid assets	50–85% RSF (depending on credit rating and market characteristics)
Cash and central bank reserves	Mostly liquid assets	0% RSF
Derivatives exposures	Contingent liabilities	100% RSF (depending on replacement cost and collateralization)
Off-balance sheet commitments	Contingent liabilities	5–10% RSF (depending on commitment type and nature)

) shows the details designed for available stable funding and required stable funding.

$$NSFR={\displaystyle \frac{\Sigma \mathrm{A}\mathrm{S}\mathrm{F} \mathrm{W}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}\mathrm{e}\mathrm{d} \mathrm{A}\mathrm{m}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{t}\mathrm{s}}{\Sigma \mathrm{R}\mathrm{S}\mathrm{F} \mathrm{W}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}\mathrm{e}\mathrm{d} \mathrm{A}\mathrm{m}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{t}\mathrm{s}}},$$

(1)

where ASF is “available stable funding”, and RSF is “required stable funding”.

The NSFR is widely treated as a liquidity measure, but it must be understood that it pertains to aspects of bank solvency. While often considered to be interconnected, these are in fact subtly different concepts, whereby solvency refers to the long-term ability of a bank to meet its obligations, while liquidity relates to the ability to do so without significant price movements. An example of the difference between these attributes can be seen in the infamous case of Northern Rock in the UK; while the bank was solvent, it suffered from a liquidity crisis as short-term funding dried up, which caused its total collapse. As such, although NSFR can be a helpful tool to assess funding stability, other measures, such as the liquidity coverage ratio (LCR), might have a more direct bearing on the evaluation of short-term liquidity resilience.

To enhance the robustness of our findings, we use an additional indicator of liquidity, the leverage ratio, which is also one of the new regulatory standards adopted by Basel III after the global crisis that hit the banking sector in 2008, and which is measured by the ratio of shareholders’ equity to assets (Tang et al., 2024).

3.3. Control Variables

Based on previous studies, a set of control variables were included to avoid error in specifying the model, as adumbrated below:

• Return on assets: net profit after tax on total assets;
• Bank size: the natural logarithm of total assets;
• Capital adequacy ratio: Tier1 + Tier2 risk-weighted assets;
• Bank age: the number of years from banks’ establishment to the year of study;
• Inflation rate: Consumer Price Index, which reflects the change in the prices of a fixed basket of goods and services consumed by individuals.

Finally, gross domestic product (GDP) was calculated as shown in Equation (2):

GDP = private consumption + gross private investment + government
investment + government spending + (exports − imports).

(2)

3.4. Study Model

The multiple regression model is applied to examine the impact of FIs on liquidity risk in commercial banks in the GCC states, as shown in Equation (3):

$$\begin{array}{c}Bank {Liquidity}_{\mathrm{it}}={\beta }_0+{\beta }_1{FINTECH}_{\mathrm{it}}+{\beta }_2{ROE}_{\mathrm{it}}+{\beta }_3{SIZ}_{\mathrm{it}}+{\beta }_4{LCR}_{\mathrm{it}}+\\ {\beta }_5{CAD}_{\mathrm{it}}+{\beta }_6{AGE}_{\mathrm{it}}+{\beta }_7{INF}_{\mathrm{it}}+{\beta }_8{GDP}_{\mathrm{it}}+{\beta }_9{\mathrm{F}\mathrm{I}\mathrm{N}\mathrm{T}\mathrm{E}\mathrm{C}\mathrm{H}}_{\mathrm{it}}\ast {SIZ}_{\mathrm{it}}+u_{\mathrm{it}}\end{array},$$

(3)

where _i denotes bank and _t is time, a fundamental concept in the banking industry. FINTECH refers to FIs, a significant trend in the finance sector. ROE represents the return on equity, a key performance indicator in banking. LCR represents the proportion of highly liquid assets that banks should hold to ensure that they can meet their short-term obligations. SIZ is the bank size. CAD indicates the capital adequacy ratio, a measure of financial strength in banking. AGE represents bank age, a factor influencing stability in the banking sector. INF is the inflation rate, a key economic indicator in the finance industry. GDP is the value added created through the production of goods and services in a country during a certain period. FINTECH*SIZ is the interaction of FIs and bank size.

4. Results

4.1. National Indicators

Figure 1 illustrates the extent of FIs in GCC countries. It can be seen that the UAE, Saudi Arabia, and Qatar are the top-ranked countries (in descending order) in terms of FIs, with a surge from 2022–2023 in the UAE. Banks in these fintech pioneers have witnessed huge investments in IT infrastructure and financial innovation, and they have benefited from extensive government support in this field (Musleh Alsartawi, 2024). For example, Saudi Arabia’s national development plan, Vision 2030, focuses on fintech as part of its strategies to develop the digital economy. The UAE developed the “Dubai International Financial Center”, which supports financial innovation and attracts startups in this sector. Countries such as these fintech leaders have attracted huge investments in the fintech sector by encouraging startups and digital business incubators (Alkhazaleh, 2021). This reflects the macroeconomic urgency of diversification away from oil and gas and public sector employment in the GCC, and a surge of public and private investment in technological innovations (Al-Maamary et al., 2017).

Conversely, despite Oman’s efforts, such as the creation of a “Digital Sultanate of Oman”, its policies and regulations have not been as robust or swiftly implemented as those in neighboring countries. Because of Oman’s conservative financial regulatory environment, FI has been less supported there (Khan et al., 2023). Additionally, Oman and Bahrain have smaller markets in terms of population compared to Saudi Arabia and the UAE, and therefore the demand for fintech may be inherently less potent in such markets (Hamadien, 2022). Omani banks may not see the same need to develop complex digital financial solutions as is the case in the UAE or Saudi Arabia, which are witnessing rapid economic growth and a larger volume of companies that rely on these solutions.

4.2. Descriptive Analysis

Table 1. Descriptive statistics.

Variables	Obs	Mean	SD	Min	Max	Skew	Kurt
FINTCH	156	0.001	0.001	0	0.004	0.746	2.797
NSFR	156	1.201	0.178	0.858	1.7	0.877	3.419
LEVE	156	0.106	0.038	0.013	0.173	−0.708	3.661
LCR	156	2.112	0.811	0.957	4.535	0.903	3.273
CAD	156	0.188	0.034	0.137	0.281	1.171	4.37
ROE	156	0.083	0.063	−0.139	0.177	−1.237	5.524
SIZ	156	10.124	0.687	8.036	11.507	−0.711	4.739
AGE	156	46.065	11.689	14	68	−0.902	3.748
INAF	156	0.019	0.019	−0.025	0.05	−0.794	2.777
GDP	156	0.012	0.04	−0.053	0.075	−0.27	1.806

describes the study variables for 26 commercial banks in GCC states from 2018 to 2023. The NSFR for the study sample was 1.201, with a standard deviation (SD) of 0.178. This result indicates that the banks in the study sample have more than the required net stable funding to support their operations, which is in line with the Basel III standard to reduce liquidity risks and enhance financial stability.

Concerning leverage, the average result was 0.106, and the SD was 0.038. This result is considered very good, as it exceeds the minimum limit specified in Basel III (0.03) by a wide and comfortable margin, which indicates strong and flexible capital, but it indicates conservative capital, which may be at the expense of profitability. With regard to FIs in commercial banks in GCC states, the average was 0.001, consistent with the results reported by Al-Shouha et al. (2024).

As regards the control variables, the results show that the average of the liquidity coverage ratio is 2.11, with an SD of 0.81. The results of capital adequacy show that the average is 0.188, with an SD of 0.034. The average ROE is 0.083, and the SD is 0.063. The average size of commercial banks in GCC states is 10.12, while the SD is 0.68. The average age is 46.06 years. The average of INF is 0.019, and the SD is 0.0. In terms of GDP, the average is 0.012, with an SD of 0.04.

Table 1 presents the skewness and kurtosis values, which signify the normal distribution of the gathered data. Kline (2023) indicates that data are deemed to have a normal distribution if the skewness value falls within the range of ±3 and the kurtosis value is within the range of ±10. Table 1 indicates that the results are satisfactory and conform to the assumption of a normal distribution for the data.

4.3. Correlation Analysis

Table 2. Correlation matrix.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(8)	(9)	(10)	(11)	VIF
(1) NSFR	1.000
(2) FINTCH	−0.062	1.000									1.45
(3) LCR	0.354	−0.154	1.000								1.40
(4) LEVE	0.051	−0.059	0.155	1.000
(5) SIZ	0.174	0.444	−0.126	−0.152	1.000						1.48
(6) ROE	0.146	−0.086	0.147	0.073	−0.208	1.000					1.48
(8) CAD	0.547	0.038	0.478	0.357	−0.017	0.224	1.000				1.61
(9) INF	−0.192	0.027	0.008	0.066	−0.140	0.222	−0.034	1.000			1.32
(10) AGE	−0.006	−0.191	0.184	0.032	−0.344	0.065	0.080	0.079	1.000		0.85
(11) GDP	−0.038	0.079	−0.112	−0.053	0.110	0.015	0.027	0.322	−0.024	1.000	1.38

shows the correlation matrix between the dependent, independent, and control variables. The results indicate that all correlation values are below the value of threat threshold, as Field (2013) indicated that the values should be less than 0.8. Therefore, there are no indicators of multicollinearity problems. However, according to Hair et al. (2010), a multicollinearity problem may arise even if it does not exceed the threshold indicated in the correlation matrix. Therefore, this study applied the variance inflation factor (VIF) test as an additional test to detect multicollinearity problems. The values should be less than 10 (Hair et al., 2010). Table 2 shows the VIF test results, where all values are within the normal limit, so the study model does not suffer from multicollinearity problems.

4.4. Regression Results

Table 3. Results of finite element method (FEM) panel-data estimation for NSFR.

Variables	Coef.	t-Value	Sig
FINTECH	−0.14	2.0	**
SIZ	−0.007	2.10	**
INAF	0.87	0.26
GDP	−0.002	1.91	*
LCR	−0.251	1.59
ROE	0.053	1.60
CAAD	0.087	2.30	*
AGE	0.01	1.9	*
FINTECH*SIZ	0.076	2.34	**
ADV-COUNT	0.064	2.44	**
CON	0.97	0.52
Mean dependent var	1.201	SD dependent var	0.178
R-squared	0.45	Number of obs	156
F-test	1.95	Prob > F	0.00
Breusch–Pagan test	279.20 *	Hausman test	72.8 *

*** p < 0.01, ** p < 0.05, * p < 0.1.

shows the diagnostic tests of the study model. The Breusch–Pagan and Hausman test results show that the fixed effects model is suitable for the study model. To account for potential heterogeneity among Gulf Cooperation Council (GCC) countries, a pooling restriction test was conducted to examine whether the relationship between financial technology (fintech) innovations and liquidity risk is consistent across countries. The test results (p < 0.05) reject the null hypothesis of parameter homogeneity, indicating that the effect of fintech on liquidity risk significantly varies across GCC countries.

Accordingly, a dummy variable was introduced to differentiate between countries with greater fintech advancement —namely the United Arab Emirates, Saudi Arabia, and Qatar (coded as 1)—and the remaining countries (coded as 0). These three countries are considered leaders in fintech adoption and regulation due to their robust digital infrastructure, progressive financial legislation, and availability of innovation funding.

The results of Table 3 indicate that the study model is statistically significant, and the R square score of 45% indicates that the study model explains 0.45 of the change in liquidity risk in banks. Thus, the statistical results support H1, demonstrating that financial technological innovations decrease the NSFR; given that the NSFR is inversely related to liquidity risk, the negative coefficient of fintech on NSFR in the baseline model implies that fintech increases liquidity risk in commercial banks.

Consumer theory and disruptive innovation theory both support this result, and there are several ways to explain this relationship. The first tendency is that FIs, like mobile payments and instant transfers, facilitate quick money withdrawals for customers, potentially causing fluctuations in liquidity as the total balances available at the bank decrease.

Second, the establishment of FIs carries high costs, and investing in such solutions inherently undermines banks’ liquidity.

Third, such financial technologies may also increase risks and pressures on banks, leading to more fragile and negatively stable liquidity, with the so-called “fragility channel” (Jameaba, 2020).

Fourth, FIs are meaningful drivers of increased market competition, resulting in a change in the provision of financial services and requiring banks to reconsider their liquidity management practices, especially those based on traditional models.

These results are in line with those of Tang et al. (2024), showing that fintech is poised to dent banks’ liquidity. The trends of innovations in fintech influence consumer decisions and preferences, and shifts in consumer behavior may negatively impact bank liquidity in traditional banks (e.g., late fintech adopters) if deposits decline when customers prefer more accessible and efficient options (such as challenger banks). Thus, banks need to adjust to these transformations in consumer behavior in order to protect the class of their liquidity. This is line with the general trend of FIs reshaping the financial ecosystem, urging institutions to adapt their strategies. Such adaptation may directly affect liquidity, either by reducing deposits or warranting the need to hold more cash commensurate with the risks to which banks are exposed. In the end, banks need to adapt and constantly grow to stay competitive and preserve liquidity stability.

The results show that the interaction between bank size and fintech plays a significant role in determining the liquidity of banks in the study model, supporting H2. The interaction coefficient between fintech and bank size indicates a moderate positive effect on bank liquidity; bank size thus plays an important role in the relationship between FIs and bank liquidity. Fintech has a greater impact on larger banks than on their smaller counterparts, which is in alignment with the implications of agency, resource, and transaction cost theories, consistent with previous studies (Tang et al., 2024). Larger banks are able to adopt greater technological innovations due to their larger financial resources and assets, which also enable them to create fintech platforms and potentially establish fintech companies.

These strategies and business models serve to broaden the range of services offered by banks, but they also exert pressure on their capital, heighten operational risks, and necessitate changes to their business models. In contrast, most small banks suffer from capital constraints that make it difficult for them to develop fintech independently. As a result, fintech has become increasingly capable of influencing large banks. There are multiple ways to interpret this finding. Large banks have significant financial liabilities, including loans and capital allocated for regulatory compliance; in this context, fintech can lead to increased capital requirements or changes in the financing structure due to additional liabilities or technological requirements, which increases pressures on liquidity.

Larger banks tend to be a little bit more complicated than smaller banks, which makes them vulnerable to the pressures caused by technological innovations. This can manifest itself when large banks roll out new fintech, which can create unexpected operational challenges that take many resources to address and sap available liquidity. Large banks are also more complex in terms of their operations, and their increasingly heavy dependence on fintech may further increase such complexity. When large banks integrate systems on top of their existing systems, they run the risk of increased operational risks that could affect liquidity. For example, if there are system problems that delay or disrupt operations, large banks may find themselves forced to use liquidity reserves to meet these risks.

In short, large banks play an important role in reinforcing the negative relationship between fintech and liquidity due to their operational complexity, systemic risks, and large liabilities.

Moreover, fintech may be a tool to deter the entry of new competitors, as large banks find it easier to comply with strict regulations that may be burdensome for emerging banks. The positive interaction between FIs and bank size also indicates that larger banks benefit from innovations to achieve a competitive advantage and dominate the market, which calls for considering regulatory policies that support fair competition and promote the entry of new players into the banking sector.

The study results indicate that older banks have reduced liquidity risk, which reflects the ability of older banks to deal with financial risks more effectively, as larger banks have more experience in addition to a strong infrastructure that helps them adopt fintech more efficiently compared to smaller and newer banks.

The results indicate that banks in countries with more advanced fintech development (i.e., the UAE, Saudi Arabia, and Qatar) exhibit differing behavior and more obvious impacts concerning the relationship between FIs and liquidity risk. These countries have a positive impact on the relationship between fintech and liquidity risk, which may be due to their legislation keeping pace with developments on the ground in the financial industry, their stable regulatory environments, or the greater availability of necessary funding.

Figure 2 displays a scatter plot depicting the relationship between the fintech index and the net stable funding ratio (NSFR) of banks. The overall distribution of the data points shows a negative slope, meaning that the greater a bank’s focus on using fintech tools, the lower the NSFR tends to be. This relationship is consistent with the results of the previous statistical analysis (Coef. = −0.14, t = 2.0, p < 0.05).

4.5. Heterogeneity Analysis

In order to determine whether the influence of financial technology (fintech) on liquidity risk was different among nations with differing fintech development levels, a heterogeneity analysis was carried out. This aligns with previous studies that found that countries with more advanced fintech ecosystems may experience different outcomes due to stronger regulatory frameworks, higher financial inclusion, and better technological infrastructure. Thus, this research proposed a dummy variable that reflects the relative advancement of fintech in Gulf countries, i.e., the United Arab Emirates, Saudi Arabia, and Qatar, termed ADV_COUNT. To account for any differential effects, we added an interaction term between fintech and ADV_COUNT.

The heterogeneity analysis results in

Table 4. Results of heterogeneity analysis.

Variables	Coef.	t-Value	Sig
FINTECH	−0.116	1.92	*
SIZ	−0.007	2.10	**
INAF	0.87	0.26
GDP	−0.002	1.91	*
LCR	−0.251	1.59
ROE	0.053	1.60
CAAD	0.087	2.30	*
AGE	0.01	1.9	*
ADV-COUNT	0.060	2.34	*
FINTECH*ADV-COUNT	0.092	2.60	**
CON	0.97	0.52
Mean dependent var	1.201	SD dependent var	0.178
R-squared	0.45	Number of obs	156
F-test	2.05	Prob > F	0.00
Breusch–Pagan Test	279.20	Hausman test	72.8

*** p < 0.01, ** p < 0.05, * p < 0.1.

show that the interaction term between fintech and ADV_COUNT is positive and significant at the 5% level. This means that the effect of fintech on liquidity risk (in terms of NSFR) is more pronounced in advanced Gulf countries than in other countries. However, the positive and significant interaction term suggests that in the countries with greater fintech advancement (UAE, Saudi Arabia, and Qatar), this negative impact of fintech on NSFR is mitigated, meaning that fintech in these countries has a less adverse or even a stabilizing effect on liquidity risk. This finding may reflect the ability of advanced countries to better regulate and integrate fintech innovations in ways that support liquidity management. Their stronger institutional frameworks, financial infrastructure, and risk-monitoring systems may reduce the potential destabilizing effects of rapid technological adoption observed in less advanced contexts.

4.6. Robustness Testing

4.6.1. Alternative Proxy for Bank Liquidity

To verify the robustness of our results, we used leverage, as in Basel III, as an alternative measure of the dependent variable and re-conducted the regression analysis (Tang et al., 2024).

Table 5. Alternative dependent variable (leverage).

Variables	Coef.	t-Value	Sig
FINTECH	−0.27	2.2	*
SIZ	−0.002	1.97	*
INAF	−0.025	−0.38
GDP	0.070	2.22	*
LCR	0.47	1.79	*
ROE	−0.267	−1.22
CAAD	−0.002	−1.70	*
AGE	−0.02	−1.7	*
FINTECH*SIZ	−0.050	1.74	*
ADV-COUNT	0.1	2.01	**
CON	0.37	0.62
Mean dependent var	0.106	SD dependent var	0.038
R-squared	0.40	Number of obs	156
F-test	1.76	Prob > F	0.00
Breusch–Pagan test	179.20 *	Hausman test	58 *

*** p < 0.01, ** p < 0.05, * p < 0.1.

shows the obtained results, which are consistent with the previous extracted results; this indicates the robustness of our main results.

4.6.2. Addressing Endogeneity: Two-Step Gaussian Mixture Model (GMM)

In this section, we re-estimated the study model using the two-step GMM method to address concerns about heteroskedasticity and serial correlation. In addition, the two-step GMM is crucial in addressing the endogeneity problem, which is a common problem in dynamic panel data models. Traditional models such as OLS are ineffective when such problems exist. Ignoring endogeneity in fintech research could potentially lead to biased results (Greene, 2018).

The study applied the two-step GMM to obtain unbiased results (Ahsan et al., 2024). The two-step GMM uses lagged values of endogenous variables, such as the dependent variable, as instruments to address endogeneity. Assuming these lagged values to be uncorrelated with errors in the current period, they serve as valid instruments to overcome the endogeneity problem.

It is important that the number of instruments is fewer than the number of groups (the units we analyze, such as companies or countries) to avoid the problem of instrument proliferation. If the number of instruments is too large compared with the number of groups, this may lead to problems in the model and reduce the reliability of the estimates. The results shown in

Table 6. Two-step GMM.

Variables	Coef.	t-Value	Sig
NSFRt-1	0.67	2.08	**
FINTCH	−1.51	2.04	**
SIZ	−0.02	−1.50	*
INAF	0.46	0.22	*
AGE	0.003	1.78	*
GDP	−0.201	−1.21
LCR	−0.02	−0.64	*
CAD	0.54	0.52	**
ROE	0.110	1.21
FINTECH*SIZ	0.084	1.99	**
ADV-COUNT	0.12	1.95	**
Constant	0.011	0.61
Hansen	0.98	No. groups	26
AR(1)	2.05	No. instruments	15
AR(2)	0.88
No. of obs	130

*** p < 0.01, ** p < 0.05, * p < 0.1.

indicate that the number of instruments (n = 14) is fewer than the number of groups (n = 26) (Roodman, 2009). The results of the Hansen test to verify the validity of the instruments used in the model indicate the tools’ validity, as the null hypothesis cannot be rejected (p > 0.05). The instruments are thus not correlated with any of the model’s error components (i.e., the no-correlation condition) (Aastvedt et al., 2021; Ghosh et al., 2023).

The Arellano–Bond test results for the presence of autocorrelation in the residual errors for the dynamic model of panel data are given in Table 6, where we can observe that the result of AR(1) is significant (p-value 0.05), which means that there is a first-order serial correlation, which is expected in this type of model (Roodman, 2009). The AR(2) result is insignificant (p-value > 0.05), confirming that the data do not have second-order serial correlation, which means that the model and the instruments used in the two-step GMM are valid.

The two-step GMM estimator shows that the lagged liquidity risk coefficient for the variable NSFR is positive and significant at 1%. This is because the lagged values have an effect on the dependent variables. Furthermore, FIs continue to have a positive impact on liquidity risk, which aligns with the primary findings of this research, as presented in the FEM results in Table 3. Also, the two-step GMM estimator showed that bank size moderates the relationship between FIs and liquidity risk, which is consistent with the primary findings of this research, as presented in the FEM results in Table 3.

5. Conclusions

This paper presents a theoretical model linking FIs, liquidity risk, and bank size, contributing to the literature on fintech and liquidity in commercial banks, as commercial banks have been adopting financial innovations in their operations to keep pace with market developments and compete with modern fintech companies. The study provided a theoretical framework for these relationships through several widely recognized and utilized financial theory frameworks (i.e., consumer, disruptive innovation, agency, resource-based, economies of scale, and transaction cost theories), consequently providing suggestions and solutions to address financial issues and financial stability in banks.

We analyzed an index of technological innovations in commercial banks from the GCC, which we constructed using data from banks’ annual reports between 2018 and 2023,

The results showed that the effect of fintech innovations on the NSFR was negative; therefore, it positively correlates with liquidity risk. The deeper reason is that installing fintech infrastructure is costly, putting pressure on banks’ liquidity in the short term. This is especially pertinent in Gulf Cooperation Council (GCC) countries, where fintech is still in its infancy. They have a dire need to build their digital platforms, ensure cybersecurity, and meet regulatory compliance. which tends to weigh down on banks financially. These costs could impact available liquid assets, which could lead to a lower NSFR and an in-crease in liquidity risk in the short term.

Moreover, the fintech index constructed in this research is based on the occurrence of fintech-related keywords appearing in the banks’ reports and disclosures. Such a method indicates the degree of attention and focus of banks on fintech in line with their strategic plan but may not necessarily be aligned with the installation of risk prevention/mitigation mechanisms or operational readiness.

Furthermore, the interaction between fintech adoption and bank size was statistically significant and positively associated with liquidity risk, implying that when banks are adopting fintech, larger banks are less prone to increased liquidity risk than smaller banks.

There are several underlying reasons that may explain this disparity. For example, larger banks generally have better technological infrastructure, stronger internal controls, and more diversified access to funding sources than smaller banks, which allows them to integrate the fintech innovations more seamlessly while at the same time reducing their liquidity risk. Small banks, on the other hand, may encounter operational difficulties, have a limited resource base, and face comparatively higher costs of adopting these types of technologies, which could increase their vulnerability to liquidity shocks. These findings emphasize that bank heterogeneity needs to be accounted for when considering the impact of fintech on the financial stability of the GCC banking industry.

The chief limitation of this study is that the selection of the study sample and the study period were limited, due to a lack of publicly available pertinent data. Future research can address the factors affecting FIs and business strategies that reduce liquidity shortages in banks and the banking system in the GCC and in other regional blocs or national economies.

The development, adoption, and stimulation of FIs play a vital role in increasing competitiveness in the financial sector, as traditional banks face fierce competition with fintech companies and challenger banks, which in turn attracts increasing attention from policymakers to regulate this emerging industry and create equal teams that enhance creative competition. In addition, banks must intensively adopt technological innovations to keep pace with the modern trends in this field while developing their strategies and business models in conjunction with emerging consumer expectations and technological and fundamental economistic realities in order to address the problems of liquidity shortages resulting from competition in the markets relative to consumer acceptance and demand for new technologies on the one hand and available resources and competitive demands within banks themselves on the other.