Assessing the impact of FinTech on bank efficiency: insights from the MENA region Open Access

The emergence of financial technology (FinTech) can be traced back to the 2008 global financial crisis, which compelled financial institutions to reduce costs and restructure their business models. This transformation has been closely associated with the rise of critical subsectors, notably cybersecurity and the rapidly developing blockchain industry (Serbulova, 2021; Elsayed et al., 2024; Afzal, Abu Khalaf, Al-Naimi, & Samara, 2025). As a result, capital has flowed more freely across borders, transaction costs have declined, productivity has improved, information asymmetries have narrowed and new investment opportunities have arisen across economies at different stages of development (Cordelia, 2006; Elsayed et al., 2024; Wali Ullah, Dey, & Khan, 2025). These innovations have consequently helped reduce operational costs and enhance customer experience (Mhlongo, 2025). Nonetheless, they also introduce significant risks, including regulatory gaps, cyber threats and higher compliance costs (Ullah & Zeb, 2023; Vijayagopal, Jain, & Ayinippully Viswanathan, 2024; Ullah & Begum, 2025).

Several countries have emerged as pioneers in FinTech innovation by adopting supportive ecosystems characterized by robust regulatory oversight, advanced digital infrastructure and public–private collaboration (Ediagbonya & Tioluwani, 2023; Kowalewski & Pisany, 2023). This approach has enabled these economies to lead in deploying cutting-edge financial technologies, including digital banking, blockchain applications and AI-driven financial services, thereby reshaping the global financial landscape (Romanov & Khubulova, 2020; Radu & Copaciu, 2021). The convergence of technology and finance has facilitated the development of sophisticated financial solutions while driving the creation of adaptive regulatory frameworks that foster and encourage FinTech growth (Chemmanur, Imerman, Rajaiya, & Yu, 2020; Anestiawati, Amanda, Khantinyano, & Agatha, 2025).

Over the past decade, FinTech adoption in the MENA region has accelerated, driven by rapid digital transformation, progressive regulatory reforms and rising demand for innovative financial services (Murinde, Rizopoulos, & Zachariadis, 2022). Consequently, MENA countries have modernized their financial institutions, positioning themselves as significant players in the global financial ecosystem. Recent industry reports estimate that FinTech revenues in the MENA region and Pakistan reached approximately USD 1.5 billion in 2022 (Remo Giovanni, 2022). This growth reflects the region’s accelerated digital transformation, particularly in Gulf Cooperation Council (GCC) countries, which have implemented strategic initiatives aimed at economic diversification, supported by government-led investments and favourable regulatory frameworks designed to attract digital financial service providers. Leading banks have concurrently intensified their digital offerings to align with evolving customer expectations, contributing to the emergence of a dynamic and diversified FinTech ecosystem. FinTech is thus undergoing rapid expansion, serving as a critical instrument for commercial banks to advance structural reforms and enhance operational efficiency (Yu, 2024).

Numerous studies have examined FinTech's impact on firms' financial performance (He, Geng, Tan, & Guo, 2023; Bronzini, Giunta, Pierucci, & Sforza, 2025; Xu, Chen, Yang, & Li, 2025), while other research has explored key determinants of FinTech development and adoption (Zavolokina, Dolata, & Schwabe, 2016; Razzaque, Cummings, Karolak, & Hamdan, 2020; Amnas, Selvam, Raja, Santhoshkumar, & Parayitam, 2023). Several studies have also investigated the effects of FinTech adoption on organizational performance, highlighting its transformative role in enhancing operational efficiency and competitive advantage (Nguyen, Tran, & Ho, 2021; Ayadi, Chiaramonte, Cucinelli, & Migliavacca, 2025). Most existing research has focused on developed and developing economies, examining the relationship between FinTech and banking performance in regions such as Europe, North America and parts of Asia (Carlini, Del Gaudio, Porzio, & Previtali, 2022; Murinde et al., 2022; Jha & Dangwal, 2025).

However, studies focusing specifically on banks in the MENA region remain scarce (Banerjee, 2020; Kammoun, Loukil, & Loukil, 2020). Moreover, the limited literature addressing this region often relies on conventional measures of bank performance (Khalaf, Awad, Ahmed, & Gharios, 2023; Kharrat, Trichilli, & Abbes, 2024), without fully accounting for structural changes and efficiency dynamics introduced by FinTech innovations. This represents a critical gap, as FinTech adoption in MENA banking may influence performance through channels not captured by traditional metrics (Abu Khalaf, Al-Sharkas, & Sarea, 2025). Our study seeks to fill this gap by providing a comprehensive assessment of FinTech's impact on banking efficiency in the MENA region using advanced analytical tools and broader performance indicators.

From a theoretical perspective, this study is informed by two complementary frameworks: the Diffusion of Innovation Theory (DoIT) and the Technology Acceptance Theory (TAT). DoIT explains how innovations spread within social and organizational systems, emphasizing attributes such as relative advantage, compatibility, complexity, trialability and observability (Rogers, 1995). TAT, in contrast, focuses on the adoption behaviour of individuals and organizations, highlighting the roles of perceived usefulness and perceived ease of use (Davis, Bagozzi, & Warshaw, 1989). By integrating these frameworks, the study captures both the organizational diffusion of FinTech innovations and the behavioural acceptance by financial institutions, providing a robust theoretical foundation for analysing the impact of FinTech adoption on bank efficiency in the MENA region.

Accordingly, the primary objective of this study is to analyse the impact of FinTech development on bank efficiency in the MENA region. Specifically, the study employs well-defined proxy FinTech variables, enabling robust empirical identification in panel models and facilitating interpretation for policymakers (Ozili, 2018). To control for firm-level heterogeneity, we include firm size and return on assets (ROA) alongside the FinTech-related variables. Bank efficiency is assessed using SFA, providing a robust examination of the relationship between FinTech adoption and operational performance within the regional banking sector.

The choice of MENA region is motivated by the unique structural and institutional qualities, which distinguish it from the rest of the region. During the last two years, the region's banking System is undergoing dramatic turmoil, characterized by rapid digitalization, the emergence of over 1,000 FinTech startups and investments of about USD 1.9 billion. Regulatory innovation, including open banking frameworks and FinTech-friendly policies, has accelerated the adoption of digital financial services. Changing consumer behaviour, reflected in the rise of Buy Now, Pay Later services, digital wallets and super apps, is reshaping traditional banking models. Furthermore, the integration of AI and blockchain technologies enhances security, efficiency and personalization, positioning MENA as a unique and dynamic context to examine the effects of FinTech adoption on banking efficiency.

This study contributes to the literature in three ways. First, it examines the effects of FinTech on both cost and profit efficiency, offering an integrated perspective rarely explored in emerging banking markets. Second, it evaluates the impact of disaggregated FinTech variables to identify which channels most strongly influence efficiency. Third, methodologically, it applies SFA to distinguish inefficiency from stochastic shocks, allowing joint estimation of cost/profit efficiency scores and their determinants within a single-step framework.

Understanding this relationship has important academic and practical implications. From a policy perspective, insights can guide regulatory frameworks that foster FinTech innovation while enhancing bank efficiency. From a managerial standpoint, findings can help financial institutions optimize digital transformation strategies to improve operational performance and service delivery. Ultimately, this study enriches the literature on digital finance by providing region-specific evidence of FinTech's role in enhancing bank efficiency in emerging financial markets, particularly in the MENA region.

The study is structured as follows: Section 2 reviews the relevant literature and develops research hypotheses. Section 3 details the methodology, Section 4 presents the dataset and descriptive statistics, Section 5 empirical findings, Section 6 discussion and Section 7 concludes.

Prior research has highlighted two complementary theoretical frameworks for understanding innovation adoption: DoIT and TAT.

The DoIT, originally proposed by Rogers (1995), conceptualizes diffusion as a communication process in which members of a community share innovations through specific channels over time. The theory emphasizes that factors such as relative advantage, compatibility, complexity, trialability and observability significantly influence the speed and extent of innovation adoption. Innovations perceived as offering clear benefits (relative advantage), aligning with existing values and needs (compatibility), being easy to understand and use (low complexity), allowing experimentation (trialability) and visible in their results (observability) are more likely to be adopted rapidly.

In the context of FinTech, this theory provides a valuable framework for understanding how financial institutions adopt technological innovations. The rate of FinTech diffusion is influenced not only by perceived benefits and ease of implementation but also by the surrounding economic and institutional environment. For example, commercial banks with adequate infrastructure and openness to innovation are more likely to adopt FinTech solutions, thereby enhancing financial performance, operational efficiency, and risk management (Dwivedi, Alabdooli, & Dwivedi, 2021). Moreover, diffusion can generate positive feedback effects on the broader financial ecosystem, accelerating sector-wide technological transformation (Zhu & Guo, 2024).

The TAT, introduced by Davis et al. (1989), focuses on why individuals choose to adopt and use new technologies. TAT emphasizes two central constructs: perceived usefulness (the belief that using a technology will enhance job performance) and perceived ease of use (the belief that using the technology requires minimal effort). These constructs are interrelated and influenced by external factors, such as institutional context, user environment and technological infrastructure (Baker-Eveleth & Stone, 2015).

Several studies have applied TAT to analyse user behaviour (Heinze & Hu, 2006; Lin & Chang, 2011; Lin, 2013). In FinTech, the theory helps explain adoption by both consumers and financial institutions, emphasizing that successful implementation depends not only on technological development but also on user perceptions of usefulness and ease of application. The results of Yaghoubi and Bahmani (2010) and Ahmad, Bhatti, and Hwang (2020) underscore the importance of these perceptions in driving adoption. In digital financial services, varying levels of digital literacy among users further shape adoption patterns. TAT also provides insights into the potential consequences of FinTech adoption on bank performance and consumer behaviour in different countries (Pooya, Abed Khorasani, & Gholamian Ghouzhdi, 2020; Alkhazaleh & Haddad, 2021).

Accordingly, this study draws on DoIT to explain the organizational and environmental determinants of FinTech adoption, while TAT captures the behavioural and perception-based factors influencing acceptance. Together, these frameworks provide a comprehensive theoretical foundation for investigating the relationship between FinTech adoption and bank efficiency in the MENA region.

Banks play a pivotal role in financial intermediation and economic stability, and a growing body of literature examines the implications of FinTech on their operational efficiency, profitability and overall performance. However, findings are heterogeneous, reflecting differences in legal frameworks, institutional structures, regional contexts and FinTech ecosystem maturity. This underscores the need to analyse FinTech's impact with sensitivity to institutional and regional factors.

In North America, empirical evidence indicates that FinTech adoption generally improves bank performance, though benefits vary across institutions. Wang, Moreira, and Liang (2024) analysed 355 banks (2010–2020) and found that FinTech integration enhances performance, with larger and state-chartered non-member banks benefiting more than smaller or federally chartered institutions. further show that external FinTech engagement, particularly through M&A, positively affects profitability (ROA and ROE), while internal innovation, such as AI patenting, does not yield equivalent gains.

In East and Southeast Asia, the impact of FinTech is equally complex. Katsiampa, McGuinness, Serbera, and Zhao (2022) report that inclusive FinTech services improve performance through better lending practices and liability management in China, particularly in national and rural banks, though risk-taking behaviour remains unaffected. Wang, Mao, Wu, and Luo (2023) find that investments in IT personnel and software reduce non-performing loan ratios, highlighting the lagged but significant role of human capital. Conversely, Zhao, Li, Yu, Chen, and Lee (2022) note that FinTech can improve capital adequacy and management efficiency but reduce profitability and asset quality, particularly in large state-owned banks, emphasizing the need for strategic alignment.

In Indonesia, Dilla, Zainir, and Shahrin (2025) show that FinTech lending can intensify competition and reduce cost efficiency, especially during the COVID-19 pandemic. These effects vary across bank size, type, and ownership, illustrating FinTech's potential to disrupt market dynamics under certain conditions.

In the Middle East, research findings are mixed. Akmal, Talha, Faisal, Ahmad, and Khan (2023) report that FinTech adoption improves institutional performance, with digital banking identified as the most valued service. However, Litimi, BenSaïda, and Raheem (2024) indicate that FinTech growth negatively impacts traditional banks’ ROA and ROE, suggesting potential disruption unless banks strategically adapt. Jordanian studies by Bashayreh and Wadi (2021) and Kayed, Alta’any, Meqbel, Khatatbeh, and Mahafzah (2024) confirm positive relationships between FinTech usage, such as ATMs, online and mobile banking, and profitability, with macroeconomic factors like GDP growth and bank size moderating the effects.

In Africa, the evidence generally supports a positive role for FinTech in enhancing bank performance, though success is contingent on strategic alignment and regulatory support. Ky, Rugemintwari, and Sauviat (2019) find that mobile money adoption improves profitability, efficiency, and stability in Sub-Saharan banks, particularly when combined with bank size, deposit mobilization and diversification. Similarly, Theiri and Hadoussa (2023) demonstrate that investments in digital channels, payment systems and cybersecurity enhance profitability, transparency, and resilience in Tunisian banks. Pham and Nguyen (2025) show that FinTech improves performance across financial, customer, internal process and learning dimensions using a Balanced Scorecard framework.

In summary, while FinTech adoption often enhances bank performance, the magnitude and direction of these effects are highly context-specific, influenced by market structure, technological capacity, institutional size, regulatory frameworks and timing. Gains in efficiency, stability, and profitability coexist with potential disruptions, underscoring the need for regionally tailored analyses.

This literature review motivates our study, which investigates the impact of FinTech adoption on bank efficiency in the MENA region. By employing Stochastic Frontier Analysis (SFA), we provide a rigorous assessment of how FinTech innovations affect operational and cost efficiency, filling a notable gap in the existing literature.

This study investigates the impact of financial innovation on bank efficiency using the one-step SFA model proposed by Battese and Coelli (1995). Unlike the traditional two-step approach, which estimates efficiency scores and their determinants sequentially, the one-step SFA jointly estimates efficiency and its drivers, thereby reducing measurement errors and omitted variable bias (Sun & Chang, 2011). The model assumes that inefficiency follows a truncated normal distribution, providing a robust and realistic framework for analysing both cost and profit efficiency.

From a theoretical perspective, the SFA technique is grounded in production and cost theory, offering a rigorous framework to decompose observed performance into two components: stochastic disturbances and inefficiency. This distinction is critical in banking research, where operational outcomes are influenced by both external factors, such as macroeconomic fluctuations, and internal managerial practices. By modelling inefficiency as a truncated normal distribution, the approach captures realistic variations in bank performance across heterogeneous institutional contexts.

Empirically, the one-step SFA enables the simultaneous assessment of cost and profit efficiency, providing clear insights into banks' input optimization and financial performance. Its suitability for panel data, such as our sample of 116 banks across 12 MENA countries over 2014–2023, allows for robust comparisons across diverse institutional and regulatory environments. Overall, the one-step SFA ensures both statistical consistency and theoretical rigor in evaluating the impact of FinTech adoption on banking efficiency. Accordingly, the cost and profit functions for panel data can be expressed as follows:

Where $TCit$ is the observed total cost of production at the t^thyear (t = 1,2, . . ., T) for the i^th bank (i = 1,2, . . ., N). $PBTit$ is the observed total profit at the t^th year (t = 1,2, . . ., T) for the i^th bank (i = 1,2, . . ., N); $Yit$ is the vector of banking output; $Pit$ is the vector of input prices. Following to Aigner, Lovell, and Schmidt (1977), $εit$ is the error term of the cost function and the profit function for the i^th bank. It is composed of two elements: $εit=vit+uit.$ where $vit$ denotes random errors capturing statistical noise and approximation errors, assumed to be independent and identically distributed as $N(0,σv2)$⁠, and $uit$ represents the non-negative inefficiency effects.

We estimate an alternative profit frontier under imperfect competition, avoiding reliance on output price data. Using the same specification as the cost function, Profit Before Taxes (PBT) serves as the dependent variable. To account for negative profits, PBT is transformed as:

where |PBT_min| is the absolute minimum PBT in the sample. The composed error term is specified as $(εit=vit−uit)$⁠. reflecting the production nature of the profit function.

We adopt the intermediation approach, specifying two primary outputs: total loans $(Y1)$ and other earning assets (⁠$Y2)$⁠. Input prices are defined as follows: labor (PL), measured by personnel expenses relative to total assets; funds (PF), measured by interest expenses relative to total deposits; and capital (PK), measured by operating expenses relative to fixed assets. Additionally, we include equity as a quasi-fixed input (netput) to account for variations in capital structure, risk preferences and regulatory compliance. Incorporating equity in this way enhances the robustness of the SFA model by capturing financial heterogeneity across banks and providing a more accurate assessment of cost and profit efficiency.

we impose symmetry conditions on the translog functional form to ensure theoretical consistency. Specifically, we apply the following parameter constraints: $βjk=βkj$ and $αhj=αjh.$ Furthermore, we assume that the cost (or profit) function is homogeneous of degree one in input prices, implying that proportional changes in all input prices lead to an equivalent proportional change in total cost (or profit), without altering input demand. This condition introduces the following restrictions:

Exploiting this linear homogeneity, we normalize the dependent variables and all input prices by the price of labour (PL). Specifically, we use: $n(TCPL)$⁠; $Ln(PBTPL+|PBTminPL|+1)$⁠;

$Ln(PKPL) andLn(EQUITYPL) instead of Ln(TC)$⁠; $Ln(PBT+|PBTmin|+1);Ln(PK)and Ln(EQUITY)$

Using a translog specification, the normalized cost function is:

Where, $LnTC$ denotes total cost, $Y$ the outputs, $P$ the input prices, and $Equity$ is included as a quasi-fixed input. Subscripts $i$ and $t$ represent bank and time, respectively. We estimate the stochastic frontier and the inefficiency determinants simultaneously via a one-step maximum likelihood (ML) procedure. The inefficiency term $mit$ is modelled as:

The vector $zit$ includes control variables such as bank size and Return On Assets (ROA) to account for heterogeneity. FinTech adoption is captured through variables such as number of ATMs, card ownership, mobile/internet banking transactions, digital bill payments, digital transactions and online purchases. These inputs help assess the link between financial innovation and both cost and profit efficiency. Full details of the dataset are provided in the following section (see Table 1).

This study uses a panel dataset comprising 116 commercial banks from 12 Middle East and North Africa (MENA) countries: Egypt, Bahrain, Iraq, Jordan, Kuwait, Saudi Arabia, Qatar, Oman, Morocco, Tunisia, Turkey and the United Arab Emirates. The dataset spans the period 2014–2023. Bank-level financial data were collected from Eikon Thomson Reuters, while FinTech indicators and macroeconomic variables were retrieved from the World Bank database.

Table 2 presents descriptive statistics for the key input and output variables used in the analysis, covering the period 2014 to 2023. The data reflect banks' role as financial intermediaries, consistent with the intermediation approach.

The input variables, price of labour (PL), price of funds (PF) and price of capital (PK), exhibit substantial variation, particularly PK, which shows a high standard deviation of 18.61 and a maximum value of $597.1$⁠, reflecting divergent capital intensity across institutions. The average total cost (TC) is approximately 1,514.62, with a standard deviation of $2,708.26$⁠, a minimum of 2.04, and a maximum of 32,431, highlighting significant differences in operational scale and resource utilization across banks.

Profitability, measured by profit before taxes (PBT), has a mean of 500.94 and a standard deviation of 933.12, with values ranging from 1,146.63 in loss to 7,390.13 in profit, indicating that while most banks are profitable, a notable proportion report loss, showing heterogeneity in financial performance. The netput variable, equity, also varies widely from 28.39 to 51,444.04, reflecting differences in capitalization and financial strength. Overall, these statistics justify using stochastic frontier analysis to evaluate cost and profit efficiency under heterogeneous conditions.

Table 3 summarizes the annual mean cost and profit efficiency scores along with overall averages for all banks in the sample. The overall mean cost efficiency score is 0.791, indicating that banks operate at 79.1% of optimal cost efficiency, with approximately 20.9% of input usage being inefficient. The mean profit efficiency score is higher at 0.836, suggesting that banks are generally more effective at generating profits than minimizing costs. This highlights that excelling in cost control does not necessarily imply maximized profitability.

Inter-temporal analysis shows a positive trend in efficiency over the study period. Cost efficiency increased from 75.7% in 2014 to 82.3% in 2023, while profit efficiency rose from 82.2% to 85.7%. These results indicate meaningful improvements in banks' operational performance over time.

This section presents the findings of the SFA assessing the impact of FinTech innovations on bank efficiency in selected MENA countries. Table 4 reports the parameter estimates for both the cost and profit inefficiency models. The models are statistically robust, as confirmed by the Likelihood Ratio (LR) test statistics of $415.274$ and $1609.749$⁠, respectively. The variance parameters (⁠$σ2$⁠) validate the adequacy of the stochastic frontier specification. Importantly, the gamma (⁠$γ$⁠) coefficients are close to 1 $(≈99%),$ indicating that nearly all variation in costs and profits is explained by inefficiency rather than random error.

Most output and input price coefficients are significant at the $1%$ or $5%$ level. An exception is the total loans variable (⁠$Y1$⁠) in the cost model, which is not significant. This reflects the low marginal cost of loan production in banks that increasingly rely on automated lending technologies, consistent with evidence from China and the U.S. showing that digital credit platforms streamline lending processes (Katsiampa et al., 2022; Wang et al., 2024). In contrast, labour, funds and physical capital prices positively and significantly affect total costs, in line with prior MENA studies (Kallel, Ben Hamad, & Triki, 2019; Kallel & Triki, 2024). Equity capital, however, exerts a negative and significant effect on both cost and profit inefficiency, highlighting the role of strong capital buffers in enhancing resilience, consistent with (Maatoug, Ayed, & Ftiti, 2019).

The variable DIG-PAY is associated with significant reductions in both cost and profit inefficiency, confirming that digital payment platforms enhance operational performance by minimizing manual processes, errors and transaction costs. This aligns with global evidence that digital payments boost efficiency and profitability in the banking sector (Kasri, Indrastomo, Hendranastiti, & Prasetyo, 2022; Alfawareh, Al-Kofahi, Erman Che Johari, & Chai-Aun, 2024; Saroy, Jain, Awasthy, & Dhal, 2023). From a theoretical perspective, this result is consistent with the TAT since the adoption of digital payment systems reflects users' perceptions of usefulness and ease of use, which are fundamental determinants of technological acceptance and continued utilization.

The effect of BUY-ONLINE is mixed: it improves profit efficiency (negative coefficient in the profit model) but worsens cost efficiency (positive coefficient in the cost model). This divergence suggests that while online purchasing strengthens revenue generation and customer relationships (Acharya, Kagan, & Rao Lingam, 2008; Stoica, Mehdian, & Sargu, 2015), transitional infrastructure and cybersecurity costs reduce cost efficiency in the short term (Bokhari & Manzoor, 2022; Wang et al., 2024). This pattern reflects DoIT's diffusion perspective, where early adoption stages often involve adjustment costs before the full efficiency benefits of innovation are realized.

For PAY-BILLS, results indicate lower cost inefficiency but higher profit inefficiency. Bill payment systems streamline operations and reduce transaction costs, but heavy initial investments and slow customer adoption may erode profitability until economies of scale are achieved (Brynjolfsson & Hitt, 1996; Hamdan, Gharaibeh, Al-Quran, & Nusairat, 2021). Similar transitional trade-offs were reported in studies of GCC and Jordanian banks, where digital services improved efficiency but required strategic monetization (Bashayreh & Wadi, 2021; Litimi et al., 2024). This finding further reflects the gradual diffusion dynamic outlined in the DoIT, whereby the benefits of innovation materialize progressively within institutional ecosystems.

The variable NUM-MOB-TRAN shows a strong negative effect on both inefficiency measures, implying that higher volumes of mobile and internet banking transactions significantly improve performance. This finding echo results from Sub-Saharan Africa and Indonesia, where digital transaction intensity enhanced resource allocation and bank productivity (Ahmed & Wamugo, 2018; Kulu, Opoku, Gbolonyo, & Tayi Kodwo, 2022; Dilla et al., 2025). The widespread adoption of mobile platforms reinforces both the TAT, through users' acceptance of convenient and accessible technologies, and the Diffusion of Innovation Theory DoIT, through the network-based dissemination of innovations.

By contrast, CARD usage is positively associated with cost and profit inefficiency. This inefficiency likely arises from fraud risks, compliance costs, and infrastructure expenses, confirming earlier observations that card technologies do not always translate into efficiency gains without mass adoption (Nam, Gup, & Kim, 2007; Chelangat, Kiprop, & Mutai, 2022). Similarly, ATM expansion increases inefficiency, consistent with prior findings that ATM networks raise operating costs and reduce overall efficiency. Similarly, ATM expansion increases inefficiency, consistent with evidence that extensive ATM networks raise operating costs and diminish overall efficiency (Ou, Hung, Yen, & Liu, 2009). These findings highlight that technological diffusion alone does not guarantee efficiency gains; adoption must be accompanied by effective integration and perceived value creation, as emphasized by both the DoIT and the TAT.

Finally, the control variables reveal that ROA and SIZE are negatively associated with inefficiency, meaning larger and more profitable banks operate more efficiently. This supports global evidence that size enhances economies of scale, diversification, and technology integration (Ky et al., 2019; Licerán-Gutiérrez, Horno-Bueno, Gómez-Ortega, & Mirza, 2025), making banks more resilient and cost-effective.

The findings of this study show that mobile and digital banking operations have a significant positive effect on both cost and profit efficiency in MENA banks. This supports the theoretical frameworks, indicating that perceived usefulness and ease of use are key drivers of technology adoption. Moreover, widespread adoption can generate network effects that enhance efficiency across banks. However, the results also reveal that ATMs and payment cards negatively affect efficiency, suggesting that infrastructure costs, maintenance requirements and slow customer adoption may offset potential benefits. The mixed results for online purchases and bill payments further underscore the transitional trade-offs characteristic of early adoption phases, confirming the Diffusion of Innovation Theory's premise that efficiency gains require time to fully materialize. These findings confirm previous empirical studies while extending the evidence to the MENA banking system. They also show that certain technologies may temporarily hinder efficiency when implementation is not strategically planned.

This study offers several implications. From a theoretical perspective, linking DoIT and TAT demonstrates that efficiency improvements depend not only on individual-level adoption but also on organizational integration and system-wide diffusion. This underscores the critical role of institutional context, robust digital infrastructure, and well-designed adoption strategies in shaping the outcomes of FinTech integration in emerging markets.

From a practical perspective, financial institutions should prioritize digital channels that clearly improve efficiency while carefully evaluating investments in traditional infrastructure. Regulators and policymakers can also accelerate adoption by implementing supportive regulatory frameworks and strengthening digital readiness.

Despite its contributions, the study has certain limitations. Emerging technologies such as AI and blockchain 4.0 were not included, although they may have important implications for efficiency. Future research should incorporate these technologies, compare conventional and Islamic banks, and examine long-term diffusion effects to provide deeper insights into the evolution of technology-driven efficiency in the MENA region.

The rapid integration of FinTech into the MENA banking sector presents both opportunities and challenges, significantly affecting bank efficiency. This study employs a single-step SFA on a panel of 116 banks across 12 MENA countries (2014–2023) to examine how FinTech adoption influences cost and profit efficiency.

Our findings indicate that mobile and digital transactions enhance both cost and profit efficiency, while traditional technologies, such as ATMs and payment cards, may introduce operational inefficiencies due to maintenance costs and client adoption challenges. Drawing on the Technology Acceptance Theory, these results highlight the importance of aligning FinTech strategies with customer preferences for perceived usefulness and ease of use.

The study has several practical and policy implications: banks should adopt strategic, user-centric approaches to FinTech integration, implement performance management systems to monitor and adjust digital investments, and prioritize channels that improve efficiency; policymakers should foster innovation through flexible regulatory frameworks, invest in digital infrastructure and training programs, and ensure risk-aware oversight to mitigate operational and cybersecurity risks.

Despite these contributions, the study has limitations. It does not incorporate emerging technologies such as artificial intelligence, advanced cybersecurity measures or FinTech 4.0 solutions, which are increasingly relevant to banking efficiency. Future research could address these gaps by integrating such technologies and conducting comparative analyses between conventional and Islamic banks. These insights would provide valuable guidance for regulators and banking executives seeking to optimize FinTech adoption, enhance operational efficiency and strengthen financial stability across diverse banking models in the MENA region.

Overall, the findings underscore that strategic FinTech adoption can enhance operational performance and competitiveness in the MENA banking sector, offering valuable guidance for both practitioners and policymakers seeking to drive digital transformation while managing associated risks.

The authors would like to thank the Editor-in-Chief, Khalil Nimer, and the Associate Editor, Hala Zaidan, as well as the two anonymous referees from the Journal of Fintech and Digital Accounting Review for their helpful comments on an earlier version of this paper. All remaining errors are the author’s responsibility.