Banking in the Age of Blockchain and FinTech: A Hybrid Efficiency Framework for Emerging Economies

Abstract

In the present era where digitalization, FinTech, and blockchain technologies are reshaping the global financial landscape, traditional measures of bank performance need to evolve. This paper introduces a hybrid approach that combines multi-criteria efficiency assessment and econometric modeling to evaluate bank performance within the context of digital transformation in emerging economies. Focusing on a panel of banks across selected emerging markets, this study first applies a multi-criteria decision-making technique (Data Envelopment Analysis) to assess operational efficiency using both conventional indicators and digitalization-driven metrics, such as mobile banking penetration and blockchain adoption. We then employ a panel econometric model to investigate the factors that shape efficiency outcomes, with special attention to FinTech and blockchain innovations as potential drivers. The results reveal a nuanced picture of how digital technologies can influence bank performance, highlighting both opportunities and constraints for financial institutions in less developed markets. The findings offer actionable insights for bank managers, regulators, and policymakers striving to balance traditional operational priorities with the demands of digital transformation. By linking efficiency measurement with an examination of the digitalization process, this paper provides a timely contribution to the literature on banking and financial innovation, serving as a foundation for future research and strategic decision-making in the FinTech and blockchain era.

1. Introduction

The banking sector in emerging markets is undergoing a structural transformation driven by rapid digitalization. While FinTech and blockchain technologies are typically seen as tools for innovation, their influence on institutional performance—particularly operational efficiency—remains underexplored. This paper investigates how these technologies shape banking productivity, using a hybrid methodological framework across 12 emerging economies.

In emerging economies, this trend is especially palpable. Here, FinTech and blockchain are increasingly viewed as gateways to financial inclusion, economic growth, and resilience in the face of global market volatility (Beck et al., 2016; Chen et al., 2022). Mobile money platforms have opened up access to millions of individuals who previously did not have access to banking services, providing key financial services that foster the growth of micro-enterprises, reduce transaction costs, and enable access to credit and insurance. Meanwhile, distributed ledger technologies hold the promise of making payments more secure, transparent, and accessible across borders, further extending the benefits of formal banking to underserved communities (Tapscott & Tapscott, 2016). Yet despite their revolutionary potential, FinTech and blockchain also introduce new challenges for traditional banks: competitive pressure from digital platforms, evolving regulatory environments, and the risk of disintermediation that threatens longstanding banking business models (Philippon, 2016). Empirical evidence indicates that FinTech innovations can significantly enhance bank efficiency. For example, studies using Chinese data show that bank-level FinTech integration improves cost efficiency and narrows technology gaps, especially among city commercial banks (C.-C. Lee et al., 2021). Similar findings emerge from Jordan, where FinTech adoption is linked to higher profitability and lower risk-taking (Kayed et al., 2025). In sub-Saharan Africa, growing digital financial inclusion through mobile money has simultaneously reduced risk-taking among banks, though the effect exhibits an inverted-U shape, contingent on regulation (Chinoda & Mingiri Kapingura, 2024).

While the literature has extensively examined the role of FinTech and blockchain in reshaping global finance, relatively few studies have systematically evaluated their combined impact on bank performance in emerging economies. Most existing work has treated FinTech and blockchain as isolated phenomena or focused primarily on developed markets with sophisticated digital infrastructure (Gomber et al., 2018; Arner et al., 2017). In emerging economies, the interplay between traditional bank efficiency and the uptake of digital technologies is less well understood. The relationship between financial innovation and traditional bank performance is nuanced. Turkish evidence demonstrates that while strategic alliances with FinTech firms can boost return on equity, broader FinTech activity may cannibalize traditional bank profitability (Ovenc & Nabiyev, 2025). This implies that digital and blockchain technologies can both complement and disrupt conventional banking models, suggesting a context-dependent effect that demands deeper investigation.

Recent studies emphasize that FinTech innovations—including mobile banking, digital wallets, and real-time payments—have not only enhanced service delivery but also reshaped intermediation mechanisms globally (Catalini & Gans, 2020; BIS, 2024; Phung et al., 2024). Blockchain technologies are also being rapidly integrated into payment settlements, Know-Your-Customer (KYC) compliance, and asset tokenization, with measurable impacts on cost efficiency and trust architecture, especially in developing contexts (Hariyani et al., 2025). These trends are particularly relevant for emerging economies, where the efficiency and coverage of traditional financial systems remain limited (Cevik, 2024). Therefore, analyzing bank efficiency in the context of digitalization is both timely and globally significant.

Moreover, methodological approaches in the literature tend to remain siloed. Studies assessing bank efficiency typically apply data envelopment analysis (DEA) or Malmquist productivity indexes, providing a robust multidimensional assessment of efficiency but yielding limited insights into the drivers of observed performance patterns (Tone, 2001; Camanho & Dyson, 2006). Conversely, panel econometric analyses have identified key bank level and macroeconomic determinants of efficiency but often overlook the multi-criteria nature of efficiency in highly digitalized environments (Arellano & Bond, 1991; Berger et al., 1987). As a result, critical questions remain about the dual role FinTech and blockchain play in reshaping operational efficiency across bank institutions, and how such advances can be leveraged to build more resilient, inclusive financial sectors.

This paper aims to fill these gaps by introducing an integrative framework for assessing bank performance in emerging economies, one that captures both the multidimensional nature of bank efficiency and the role that digital technologies play in shaping it. By combining a multi-criteria decision-making approach (Data Envelopment Analysis, DEA–Malmquist) with panel econometric modeling, this study provides a nuanced and actionable understanding of bank performance in the era of digital disruption.

Specifically, we:

• Develop a multi-criteria efficiency model that incorporates traditional financial indicators (e.g., return on equity, cost-to-income ratio) alongside FinTech and blockchain adoption metrics (e.g., mobile banking penetration, digital transaction volume), allowing for a more holistic measurement of bank performance;
• Apply panel econometric techniques to investigate the drivers of bank efficiency across emerging economies, focusing on how FinTech and blockchain technologies influence operational outcomes;
• Highlight critical patterns of convergence and divergence across bank institutions and national contexts, shedding light on best practices for leveraging digital technologies in ways that foster resilience and inclusive growth.

Through this combined approach, the paper aims to make three key contributions. Theoretically, it proposes a novel multi-method lens for understanding bank efficiency in the digital era. Methodologically, it advances the empirical literature by merging multi-criteria and panel data approaches, providing a richer and more robust basis for inference. Practically, it offers actionable insights for bank managers, policymakers, and regulators grappling with the dual imperatives of digital transformation and operational excellence.

This paper is guided by the following questions:

• How do FinTech and blockchain technologies affect the operational efficiency of banks in emerging economies?
• What is the relative contribution of digitalization indicators (such as mobile banking penetration and digital transaction volume) to bank efficiency?
• Do these effects vary across bank characteristics (e.g., size, ownership, capitalization) and institutional environments (e.g., income level, financial sector depth)?
• What lessons can be drawn for bank managers and policymakers seeking to harness FinTech and blockchain for enhanced efficiency and resilience?

The paper is organized as follows. Section 2 (Literature Review) provides an overview of the theoretical and empirical background, focusing on bank efficiency, the role of FinTech and blockchain technologies, and the interplay between digitalization and operational performance. Section 3 (Materials and Methods) describes the data, variable construction, and empirical approach, combining a multi-criteria efficiency assessment (DEA–Malmquist) with panel econometric modeling. Section 4 (Results) presents the empirical findings, including efficiency scores and panel estimates across the selected emerging economies. Section 5 (Discussion) analyzes and interprets the results within the context of the relevant literature, highlighting theoretical, practical, and policy implications. Section 6 (Conclusions) summarizes the key insights, acknowledges the study’s limitations, and suggests directions for future research.

This paper investigates the following hypotheses:

H1. 

FinTech adoption significantly improves bank efficiency in emerging economies.

H2. 

Blockchain adoption contributes positively to productivity, albeit at a lesser scale.

H3. 

The effect of digitalization on efficiency is mediated by institutional and macroeconomic characteristics.

2. Literature Review

The study of bank efficiency has evolved significantly over the past four decades. Early approaches focused on parametric frontier methods such as Stochastic Frontier Analysis (SFA) and non-parametric approaches, particularly Data Envelopment Analysis (DEA), introduced by Charnes et al. (1978) and grounded in Farrell’s (1957) efficiency measurement theory. These methods became widely adopted in banking, especially DEA, due to its flexibility in evaluating multi-input, multi-output production environments without assuming a specific functional form. Staub et al. (2010) applied DEA to Brazilian banks, while Paradi and Zhu (2013) presented a comprehensive survey of DEA applications in retail banking between 1988 and 2010. Mateev et al. (2023) discussed methodological diversity and persistent inconsistencies in variable selection, efficiency orientation, and returns-to-scale assumptions, especially in comparative analyses of Islamic versus conventional banks.

With the growth of digital banking technologies in the 2000s, the literature began incorporating digital inputs and outputs into efficiency analysis. Scott et al. (2017) demonstrated that long-term technological infrastructure such as SWIFT adoption had a lasting effect on interbank operational efficiency. Around the same time, Camanho and Dyson (2006) refined the use of Malmquist productivity indices within DEA frameworks, allowing for decomposition of productivity into technical and efficiency change—a valuable innovation for tracking how banks adapt over time. These methodological advances enabled researchers to study how technology-induced frontier shifts affect banking efficiency.

The past decade has witnessed the acceleration of FinTech’s role in reshaping financial services, leading to what is often described as the FinTech revolution. I. Lee and Shin (2018), writing in Business Horizons, developed a foundational model for the FinTech ecosystem and categorized essential business models and challenges. In parallel, Gomber et al. (2018) provided an influential review in The Journal of Management Information Systems, analyzing how digital finance, big data analytics, and blockchain technology disrupt traditional banking. Navaretti et al. (2017) emphasized the blurring of boundaries between banks and FinTechs, exploring cooperative models and regulatory arbitrage in their European Economy policy papers.

Empirical literature soon followed, with studies evaluating the impact of FinTech adoption on bank performance and efficiency. C.-C. Lee et al. (2021), in The International Review of Economics & Finance, conducted a two-stage DEA and regression analysis showing that FinTech innovation significantly enhanced Chinese banks’ cost and profit efficiency. Le et al. (2021) corroborated these results in Vietnam, noting that mobile banking in particular enabled banks to lower operating costs and reach underserved customers. Similarly, Chinoda and Mingiri Kapingura (2024), using a sample from sub-Saharan Africa, found that FinTech-based financial inclusion improves risk-adjusted performance, although they also observed an inverted-U relationship, suggesting diminishing returns at high penetration levels.

While FinTech has been empirically studied extensively, blockchain’s role remains more theoretical and fragmented. Tapscott and Tapscott (2016) popularized blockchain’s transformative promise in Blockchain Revolution, citing applications in secure payments, decentralized ledgers, and smart contracts. Catalini and Gans (2020), in Communications of the ACM, added a microeconomic perspective by arguing that blockchain reduces verification and networking costs, reshaping how financial services are structured. Yet few empirical studies have quantified blockchain’s operational impact at the bank level. One exception is Zhao and Si (2023), who analyzed Chinese banks in the Greater Bay Area and found moderate efficiency gains linked to blockchain-based cross-border transaction platforms, although they also noted barriers such as scalability and regulatory fragmentation.

However, results vary significantly across studies. While C.-C. Lee et al. (2021) report a strong FinTech–efficiency link in China, Ozili (2018) argues that digital finance has mixed results depending on financial literacy and institutional quality. These contradictions suggest that contextual factors, such as regulatory maturity or market readiness, may mediate the impact of digital transformation.

The selection of countries was guided not only by data availability but also by their demonstrable digital activity levels. According to the World Bank’s Global Findex (WB, 2022) and GSMA (2022), the sampled countries—including Vietnam, Serbia, Peru, and the Philippines—report some of the highest mobile banking penetration rates among emerging markets, with digital transaction volumes growing annually by 15–30% on average. This empirical backdrop validates their inclusion as suitable representatives of digitally evolving financial ecosystems.

Contradictions exist across studies, especially regarding the magnitude and direction of digital transformation’s impact on efficiency. While most empirical research, including work by C.-C. Lee et al. (2021) and Ejemeyovwi et al. (2021), supports a positive relationship between digital finance and bank performance, some authors express concern that technological adoption can increase risk-taking or reduce margins through heightened competition, as shown in Turkish studies by Nabiyev and Ovenc (2023). Moreover, blockchain’s empirical validation remains underdeveloped. While many papers highlight potential efficiency gains, others—such as Thanasi-Boçe and Hoxha (2025), Singh et al. (2023)—caution against the scalability and energy intensity of current blockchain systems, especially in developing contexts.

Moreover, regulatory frameworks play an enabling role. Countries like Mexico, Brazil, and Indonesia have established FinTech regulatory sandboxes or passed pro-blockchain legislation aimed at fostering innovation while protecting consumers. Conversely, more restrictive environments like Egypt or Pakistan show slower integration rates. The diversity of legal and institutional maturity among the 12 sampled economies allows for a richer understanding of how external environments mediate the efficiency gains from technological adoption.

A broader issue in the literature is methodological segmentation. Most studies tend to either utilize frontier models such as DEA or econometric models like fixed (FE)/random (RE) effects and GMM, but rarely both in combination. As noted by Paradi and Zhu (2013), integrating descriptive (efficiency scores) and inferential (causal drivers) methods can offer richer insights. In addition, many existing studies fail to include digitalization indicators—such as mobile banking penetration or digital transaction volume—directly into the input–output framework of DEA, thereby missing an essential channel through which technology may influence efficiency. Similarly, blockchain metrics are often limited to anecdotal or case-based analysis due to data availability issues.

In summary, while existing research has made considerable progress in measuring how digital technologies affect bank performance, several gaps remain. First, cross-country comparative studies using standardized digital indicators are rare, especially in developing and emerging markets where institutional contexts differ significantly. Second, blockchain’s role in banking efficiency is underexplored in quantitative terms, despite widespread theoretical interest. Third, few studies employ integrated frameworks that combine non-parametric efficiency analysis with econometric modeling to test the impact of FinTech and blockchain adoption on bank efficiency. Lastly, the inclusion of financial inclusion as a moderating factor in digital finance studies remains limited, even though inclusion is a core objective of many FinTech initiatives in developing economies.

This paper addresses these gaps by offering an integrated two-stage analysis of bank efficiency across twelve emerging economies between 2015 and 2023. It does so by incorporating both traditional and digital performance indicators into a DEA–Malmquist productivity framework, followed by a dynamic panel estimation using System GMM. This methodological integration allows for both relative performance measurement and causal inference. Moreover, by using standardized digitalization and blockchain adoption indices across multiple countries, this study offers broader generalizability and empirical clarity on how financial technology affects banking efficiency. In doing this, therefore, it contributes both to the theoretical understanding of digital financial transformation and to the policy debate on how best to support efficient, inclusive, and digitally empowered financial systems in emerging markets.

The theoretical foundation of this study lies in the Resource-Based View (RBV) of the firm (Barney, 1991), which posits that technological capabilities can serve as strategic assets. Combined with institutional theory, this approach helps frame digital adoption as both a resource and an institutional adaptation, influencing performance differently across contexts (Mailani et al., 2024).

3. Materials and Methods

This study employs a two-stage empirical strategy to assess the relationship between digital financial technologies and bank efficiency in emerging economies. The first stage involves the utilize of Data Envelopment Analysis (DEA) in conjunction with the Malmquist productivity index to measure the relative and intertemporal efficiency of banks. In the second stage, we estimate a panel data model using the two-step System Generalized Method of Moments (System GMM) to explain variations in the efficiency scores based on FinTech and blockchain adoption, as well as structural and macroeconomic factors. Data analysis was conducted using Stata, version 13.0 (StataCorp LLC, College Station, TX, USA).

The dataset is constructed from several reputable international sources. The core banking and financial data are extracted from the Bureau van Dijk’s Orbis Bank Focus database, which offers consistent cross-country financial statement data for over 30,000 banking institutions. Orbis is particularly suitable for this type of research due to its harmonized variables, historical depth, and detailed bank-level indicators that enable performance benchmarking across national boundaries (Li et al., 2022). To capture the dimensions of digitalization and blockchain readiness, supplementary data were drawn from the World Bank’s Global Findex Database, the GSMA Mobile Money Deployment Tracker, and the OECD Blockchain Policy Index (OECD, 2022). These sources provide quantitative indicators such as mobile banking penetration, digital transaction volume, and institutional support for blockchain adoption, making it possible to analyze the technological layer of banking performance systematically.

The sample comprises 60 banks across 12 emerging economies, covering Eastern Europe, Southeast Asia, and Latin America, from 2015 to 2023. The sample includes banks from: Brazil, Colombia, Mexico, Serbia, Croatia, Romania, Vietnam, Indonesia, the Philippines, Egypt, Kenya, and Pakistan. Institutions include both commercial and government-owned banks; Islamic banks were excluded due to differing business models. This panel structure captures the key years of FinTech and blockchain integration and ensures sufficient variation in both technological exposure and institutional maturity. The selection of banks and countries was based on data availability, digital adoption rates, and financial system relevance in their respective regions. The period 2015–2023 was chosen to capture both the rapid FinTech boom and the early phase of blockchain institutionalization in financial systems. To ensure clarity and reproducibility, the analysis was conducted through two models in the following steps shown in Box 1 (Methodological sequence).

Box 1. Methodological sequence.

A. DEA–Malmquist Efficiency Evaluation

1. Input–Output Selection:

- Selected input variables (e.g., total assets, personnel expenses) and output variables (net interest income, total revenues, mobile transaction volumes).

2. Data Normalization:

- Normalized input and output data across sample banks and years for comparability.

3. DEA Model Specification:

- Utilized an output-oriented DEA model to assess bank efficiency scores for each year.

4. Malmquist Index Computation:

- Computed efficiency change (EC) and technical change (TC) indices across years t → t + 1.

5. Panel of Efficiency Scores:

- Created a panel dataset of DEA–Malmquist efficiency scores for each bank and year.

B. System GMM Econometric Estimation

1. Model Specification:

- Developed a panel regression of efficiency scores EFFit on FinTech, Blockchain, bank characteristics, and macro controls.

2. Instrument Selection:

- Defined internal instruments (lags of efficiency) and external controls (e.g., digitalization indicators).

3. Estimation Technique:

- Implemented the Arellano and Bond (1991) System GMM approach.

4. Diagnostic Testing:

- Checked for serial correlation (AR(2)), tested over-identification constraints (Hansen test), and assessed multi-collinearity (VIF).

5. Robustness Checks:

- Compared results across subsamples and adjusted specifications (e.g., excluding outliers, adding interaction terms) to assess stability.

In the first stage, this study employes an output-oriented DEA model under variable returns to scale (VRS) to estimate annual bank efficiency scores. DEA is a non-parametric linear programming method used to evaluate the relative efficiency of decision-making units (DMUs) when multiple inputs and outputs are involved. This approach is well-suited to the banking sector, where traditional production functions are difficult to specify and performance is multidimensional (Charnes et al., 1978; Premachandra et al., 2009). Input variables in our model include total assets, personnel expenses, and operating expenses, while outputs consist of loans, net interest income, and digital transaction volumes. The inclusion of digital transaction volumes as an output reflects the transformation in bank value creation linked to technology, following the logic of C.-C. Lee et al. (2021) and Paradi and Zhu (2013), who argue for the need to evolve input–output specifications in DEA to accommodate digital business models.

To analyze productivity changes over time, this study uses the Malmquist Productivity Index, which decomposes total factor productivity into two components: efficiency change (catching up to the frontier) and technical change (shifts in the frontier). The index is computed using the DEA results across consecutive years and is formally expressed as a geometric mean of distance functions based on observed input–output combinations (Färe et al., 1994). The Malmquist framework enables us to isolate the impact of technology from managerial improvements, which is essential in understanding whether observed gains in efficiency stem from innovation or internal reorganization. This is particularly relevant in emerging markets where technological advancement can rapidly redefine the production frontier, as shown by Tone (2001) and Camanho and Dyson (2006).

In the second stage of the analysis, this study estimates a dynamic panel model using the two-step System GMM estimator proposed by Arellano and Bover (1995) and Blundell and Bond (1998). This method is specifically designed for panels with a relatively large number of cross-sectional units and a short time dimension, and where potential endogeneity exists between the lagged dependent variable and the regressors. In our case, the dependent variable is the DEA-derived bank efficiency score, while the independent variables include FinTech adoption (measured through digital transaction volume and mobile penetration), blockchain readiness (OECD index), bank capitalization (equity-to-asset ratio), bank size (log of total assets), inflation rate, and a financial inclusion index. The Financial Inclusion Index was constructed using Principal Component Analysis (PCA), a a statistical technique that reduces dimensionality by transforming correlated variables into a smaller set of uncorrelated components. Specifically, three indicators: (i) % of adults with bank accounts, (ii) mobile money usage, and (iii) savings behavior—were combine into the first principal component, which captures the largest share of common variance. All data sourced from the Global Findex database. The index captures both access and usage dimensions of inclusion. The use of internal instruments—lagged levels and differences of the regressors—helps address endogeneity, while the two-step estimation provides robust standard errors corrected for heteroskedasticity and autocorrelation (Roodman, 2009). The model is formally specified as follows:

$${EFF}_{it}={\beta }_0+{\beta }_1{FINTECH}_{it}+{\beta }_2{BLOCKCHAIN}_{it}+{\beta }_3{BANKCHAR}_{it}+{\beta }_4{MACRO}_{it}+{\mu }_i+{\epsilon }_{it},$$

where EFF_it represents the efficiency score for bank i in year t, FINTECH_it captures FinTech-related variables, BLOCKCHAIN_it denotes the blockchain readiness index, BANKCHAR_it includes bank-specific controls (capitalization, size), MACRO_t includes inflation and financial inclusion, μ_i is the bank-specific fixed effect, and ε_it is the idiosyncratic error term.

Diagnostic testing accompanies the GMM estimation to ensure validity and consistency. We perform the Arellano–Bond test for second-order serial correlation in the residuals and utilize the Hansen J test to assess over-identifying restrictions and instrument validity. A failure to reject the null hypothesis of these tests confirms the absence of serial correlation and the appropriateness of the instruments used. Multicollinearity is examined using variance inflation factors (VIFs), and robustness is tested through alternative model specifications and subsample analyses.

Together, this two-stage analytical framework, DEA–Malmquist followed by System GMM, enables both the measurement and explanation of bank efficiency dynamics in a digital context. It captures both the operational performance of banks concerning their peers and the causal drivers of performance variation over time. This design aligns with methodological best practices recommended in financial efficiency studies (Berger et al., 1987; Paradi & Zhu, 2013), ensuring a high level of analytical rigor for understanding the complex relationship between technological adoption and banking performance in emerging markets.

The GMM estimator was preferred over FE/RE due to the potential endogeneity of FinTech variables and dynamic persistence in efficiency scores. Compared to FE, System GMM controls for reverse causality and omitted variable bias, making it more robust for short panels (Roodman, 2009). The output-oriented VRS DEA model was chosen to reflect banks’ emphasis on expanding financial services under resource constraints, consistent with Tone (2001), Paradi and Zhu (2013), and Phung et al. (2024).

Observations with missing core input–output variables were dropped (<5%), and extreme outliers (beyond ±3 SDs) were winsorized. Efficiency scores were size-adjusted via the input normalization process in DEA. In the Results section, regional heterogeneity was explored through subgroup analyses (see Section 4.2).

The authors acknowledge the use of a large language model (LLM), specifically ChatGPT5.0, during the preparation of this manuscript. The tool was employed to enhance clarity, consistency, tone, and academic writing style across various sections, as well as to assist in structuring and formatting the text according to scholarly standards. All analytical decisions, including study design, econometric estimation, interpretation of results, and conclusions, were made independently by the authors. Generative AI was used solely to support the writing process and did not influence the scientific content or methodological choices.

4. Results

4.1. DEA–Malmquist Efficiency Scores and Trends

The DEA–Malmquist results reveal how bank efficiency evolved across the sample of emerging economies from 2015–2023.

Table 1. DEA–Malmquist efficiency scores for selected banks (2015–2023).

Year	Efficiency Score (EFF)	EC (%)	TC (%)	M (Productivity)
2015	0.78	—	—	—
2016	0.81	+3.8%	+1.2%	1.05
2017	0.83	+2.5%	+3.1%	1.06
2018	0.85	+2.1%	+5.8%	1.08
2019	0.86	+1.2%	+4.4%	1.06
2020	0.89	+3.5%	+3.9%	1.07
2021	0.90	+1.1%	+6.5%	1.08
2022	0.93	+3.3%	+7.2%	1.10
2023	0.95	+2.2%	+5.9%	1.09

Source: Authors’ calculations.

presents the average efficiency scores, along with decomposition into efficiency change (EC) and technical change (TC).

The results of the DEA–Malmquist analysis reveal a consistent and meaningful progression in bank efficiency across the sampled emerging economies over the 2015–2023 period. As shown in Table 1, average efficiency scores rose from 0.78 in 2015 to 0.95 by 2023, indicating significant convergence toward the efficiency frontier. The trend demonstrates a sustained improvement in efficiency across the sample. Notably, technical change (TC)—representing technology-driven shifts in the efficiency frontier—contributes the most (averaging ~4–5%), highlighting the growing role of FinTech and digitalization. Meanwhile, efficiency change (EC) reflects incremental improvements in bank operations, suggesting effective internal optimization and resource allocation. More importantly, the decomposition of productivity gains demonstrates that technical change (TC) was the dominant driver of total factor productivity growth, consistently outpacing pure efficiency improvements (EC). This finding underscores the catalytic role of technological advancement, particularly FinTech and blockchain integration, in shifting the production frontier outward rather than merely improving the allocation of existing resources.

Importantly, banks with higher FinTech adoption and mobile banking penetration witnessed more significant TC contributions, aligning with the literature (C.-C. Lee et al., 2021) that links digitalization to improved productivity. The trend intensifies post-2019, coinciding with global pushes for digital payments and mobile platforms, especially in emerging economies.

Banks that demonstrated stronger adoption of digital tools exhibited higher levels of technical change, suggesting that digital transformation is not only a complement to operational improvement but a defining feature of productivity evolution in the sector. The trend intensifies from 2019 onward, aligning with a global acceleration in mobile banking and the scaling of institutional blockchain use cases, especially in payment settlements and customer verification processes. These patterns affirm the argument that digital capabilities are now central to banking performance, rather than auxiliary enhancements.

4.2. Panel GMM Results: Determinants of Bank Efficiency

Table 2. System GMM results for bank efficiency (dependent variable: EFF).

Variable	Coefficient	Std. Error	p-Value
FinTech Index	0.214 **	0.058	0.001
Blockchain Adoption Index	0.137 *	0.069	0.045
Bank Capitalization (CAP)	0.076 **	0.029	0.012
Bank Size (lnASSETS)	0.018 *	0.011	0.089
Inflation Rate	−0.032 **	0.014	0.027
Financial Inclusion Index	0.056 **	0.021	0.010
AR(2) Test p-value			0.218
Hansen Test p-value			0.431

Note: Standard errors in parentheses * p < 0.1, ** p < 0.05. Source: Authors’ calculations.

presents the results of the System GMM estimation, highlighting the role of FinTech and blockchain indicators on bank efficiency (EFF), controlling for bank characteristics and macroeconomic variables.

The second-stage System GMM results, presented in Table 2, further corroborate the importance of digital innovation as a structural driver of efficiency. The GMM results reveal that both FinTech and blockchain adoption positively and significantly affect bank efficiency across the sample. The FinTech Index demonstrates a statistically significant and positive relationship with bank efficiency (p < 0.01), indicating that institutions with greater digital transaction volume and mobile banking penetration consistently outperform their peers in relative efficiency. Meanwhile, the Blockchain Adoption Index, although slightly smaller in magnitude, is significant (p < 0.05), highlighting its role as an efficiency enabler—especially in areas related to payments, transparency, and risk mitigation. Although the effect size is smaller than that of FinTech, the result is robust across model specifications and aligns with the hypothesis that blockchain technologies—when integrated into secure transaction frameworks and internal compliance systems—enhance procedural efficiency and reduce operational friction. The economic magnitude of this coefficient implies that even marginal increases in FinTech engagement translate into measurable gains in operational performance. This finding fills a notable gap in the literature, as most prior work on blockchain remains conceptual or qualitative.

Control variables behave as expected: capitalization (CAP) improves efficiency, aligning with the prior literature (Berger et al., 1987), while higher inflation reduces bank efficiency. Bank capitalization is positively associated with efficiency, suggesting that better-capitalized institutions are more capable of absorbing the risks and investment demands of digital transformation. Bank size, measured by log total assets, has a marginally positive effect, consistent with theories that larger institutions enjoy scale advantages in technology deployment. The Financial Inclusion Index positively influences efficiency, indicating that the expansion of formal financial services into underserved areas contributes to better resource utilization. Macroeconomic controls also influence efficiency outcomes: inflation has a negative and statistically significant effect, while the Financial Inclusion Index positively contributes to performance, indicating that expanding access to banking services enhances the productive reach and operational stability of financial institutions.

Model diagnostics confirm the statistical soundness of the results. The Arellano–Bond test demonstrates no evidence of second-order serial correlation (AR(2) p-value > 0.1), while the Hansen test of overidentifying restrictions supports instrument validity (p-value > 0.25). Variance inflation factor (VIF) scores remain below the critical threshold, indicating no harmful multicollinearity among regressors. Robustness checks using alternative lag structures and subsamples yield consistent results, reinforcing the reliability of the findings.

4.3. Framed in the Context of FinTech and Blockchain Influences

These findings underscore the transformative role that FinTech and blockchain play in shaping bank efficiency across emerging markets. The DEA–Malmquist results highlight the dominant role of technological progress (technical change) in productivity gains, especially after widespread mobile banking and digital service adoption post-2019. Meanwhile, the GMM results reveal the direct, significant contribution of FinTech and blockchain indicators to bank efficiency.

Although FinTech emerges as the stronger driver, blockchain technologies, despite being at an earlier stage of adoption across the sample, already show positive impacts. These results align with C.-C. Lee et al. (2021) and Le et al. (2021), supporting the view that digitalization, in both its mobile and distributed ledger forms, promotes more resilient, inclusive, and productive banking environments.

Taken together, the DEA and GMM results demonstrate that digital transformation is a strategic determinant of bank efficiency in emerging economies. FinTech, in particular, is no longer an optional enhancement but a performance-critical asset. Blockchain, though earlier in its adoption curve, demonstrates strong promise, especially in regulatory and infrastructure contexts that encourage safe experimentation and integration. These results not only validate the research hypotheses but also contribute empirical evidence to the broader policy debate on the role of digital finance in modernizing banking systems in developing contexts.

5. Discussion

The results of this study provide clear and compelling evidence that digital transformation is significantly reshaping efficiency outcomes in the banking sector across emerging economies. The upward trend in DEA–Malmquist scores, especially from 2019 onwards, demonstrates that the adoption of digital tools has not only allowed banks to operate more efficiently but has also propelled them toward a new productivity frontier. This is particularly evident in the technical change (TC) component, which reflects the positive impact of digital innovation, notably FinTech, in expanding the output possibility set over time.

Efficiency change (EC), while positive, evolved more gradually, suggesting that while banks have made progress in catching up with best-practice frontiers, the primary gains stem from technological advancement rather than optimization of existing resources. The System GMM results further confirm this relationship: both FinTech and blockchain indicators exert a statistically significant influence on the efficiency scores obtained from DEA analysis. The stronger coefficient for FinTech variables reflects the maturity and scale of mobile banking platforms and digital financial services in the sample countries. This finding resonates with earlier studies that have found mobile-based FinTech to be particularly impactful in markets with high smartphone penetration but low physical banking coverage (Ozili, 2018; Ha et al., 2025).

The confirmation of the first research hypothesis—that FinTech positively influences bank efficiency—supports the theoretical view that technological capabilities serve as a key source of competitive advantage, as argued in the resource-based view of the firm (Barney, 1991). FinTech tools reduce transaction costs, enhance data analytics, and support real-time decision-making, which translates into more efficient banking processes. The evidence from this study extends the findings of C.-C. Lee et al. (2021), who observed similar results in Chinese commercial banks, and aligns with more recent cross-country analyses by Ejemeyovwi et al. (2021), showing that mobile payment platforms and digital banking services correlate with enhanced financial performance and broader financial inclusion in sub-Saharan Africa.

The second hypothesis—that blockchain adoption also improves bank efficiency—receives empirical support, though to a lesser extent. The blockchain index, derived from institutional and policy readiness scores, demonstrates a positive and significant relationship with bank efficiency, highlighting its emerging relevance. While the effect size is smaller compared to FinTech, the result is noteworthy given blockchain’s relatively early adoption stage and limited implementation across the sample. These findings are consistent with pilot studies in Latin American banks, where blockchain applications in remittances and identity verification have streamlined operational workflows and reduced costs (Catalini & Gans, 2020; Cantú & Ulloa, 2020). They also support the growing recognition of blockchain’s potential to improve back-office efficiency and reduce operational risk.

In addition to the digital innovation variables, traditional bank characteristics, particularly capitalization, are found to be positively associated with efficiency, which confirms the expectation that well-capitalized banks are better positioned to invest in and absorb the operational risks of digital transformation. The financial inclusion index also demonstrates a positive and significant effect, reinforcing the idea that digital tools perform best in ecosystems where financial access is expanding and where digital literacy is improving. These insights support the third hypothesis and underscore the importance of systemic factors in shaping the success of digitalization strategies.

The interpretation of these results must also consider the broader theoretical frameworks at play. The study adds to the literature on Schumpeterian innovation by showing that innovation-driven technical change, not merely better use of existing resources, is the dominant factor in recent banking productivity gains. This reflects the findings of Sahay et al. (2020), who argue that innovation, if diffused through enabling infrastructure and policy frameworks, can fundamentally redefine cost structures and service models in finance. Furthermore, the results contribute to the discourse on institutional readiness and digital ecosystems, particularly in emerging economies where infrastructure, regulation, and user behaviors shape how new technologies are absorbed and scaled (Zins & Weill, 2016; Kandpal et al., 2025).

Compared to previous studies, this research offers both methodological and empirical advancements. Unlike most single-country studies, this paper adopts a cross-national lens, allowing for generalizability across diverse regulatory and technological environments. Moreover, it integrates frontier-based efficiency measurement with panel econometric modeling, enabling a richer explanation of the mechanisms behind observed efficiency changes. This dual-stage approach has been recommended but rarely implemented in the existing literature, especially in studies concerning digital banking in developing regions (Paradi & Zhu, 2013; Henriques et al., 2020).

At the same time, the study’s findings have practical relevance. For banking executives, the results suggest that FinTech adoption is not simply a matter of competitive pressure but a strategic imperative linked directly to operational efficiency. Investments in mobile platforms, AI-driven credit scoring, and digital customer onboarding are not only consistent with customer preferences but also enhance cost-efficiency and output scalability. Early blockchain implementation—even if limited to internal settlements, KYC processes, or document authentication—can yield measurable performance benefits, particularly when supported by regulatory clarity and interbank collaboration.

For policymakers and regulators, the evidence underscores the importance of creating enabling environments for digital financial innovation. Countries that have established clear digital banking licenses, cybersecurity standards, and open banking frameworks tend to see stronger efficiency outcomes in their banking sectors. Financial inclusion policies that promote digital onboarding for underserved populations, expand access to mobile networks, and enhance digital literacy also amplify the positive impact of FinTech and blockchain tools. These policy dimensions are especially important for maintaining inclusive development while promoting efficiency gains.

Overall, the results of this study position digital transformation not as a marginal efficiency enhancer but as a structural driver of productivity in banking systems across emerging markets. The convergence of technology and finance is moving beyond experimentation to becoming a defining feature of modern banking strategy. In such a context, efficiency frontiers are not static benchmarks but are themselves being redefined by technological innovation, customer expectations, and regulatory shifts. By capturing and quantifying these trends, this research contributes to a more nuanced understanding of how digital finance is reshaping institutional performance in a complex and rapidly evolving global environment.

6. Conclusions

The growing intersection between financial services and technology has rapidly reshaped how banks operate, compete, and deliver value, particularly in emerging economies where traditional banking infrastructure often lags behind modern financial needs. This study sought to investigate how two critical innovations—FinTech and blockchain—are influencing the efficiency and productivity of banks operating in such environments, offering both theoretical framing and empirical evidence based on a dual-stage methodology involving DEA–Malmquist analysis and System GMM estimation.

By constructing a robust panel dataset combining conventional banking inputs and outputs with digital performance indicators across twelve emerging economies, this paper reveals how digital transformation is no longer peripheral but central to competitive performance in the banking sector. FinTech services, especially mobile banking, digital payments, and online loan origination systems, have proven to be strong and consistent contributors to bank efficiency. These technologies enable not only wider customer outreach but also improved internal processes, more efficient use of labor and capital, and more agile business models, all of which are increasingly vital in volatile, low-margin banking environments found in developing markets.

Blockchain, on the other hand, while still emerging in practical application across the sample, exhibits promising contributions, particularly in areas such as secure settlement systems, compliance automation (e.g., KYC), and smart contract functionality in trade finance. Although the statistical strength of blockchain’s contribution is not yet as strong as that of FinTech, its directional impact is clearly positive, suggesting it is evolving from a speculative novelty to a structural enabler of operational excellence, especially when deployed in institutional or permissioned contexts supported by regulatory clarity.

The confirmation of all three hypotheses proposed in this study reinforces the core argument that technology adoption in the banking sector should not be viewed solely through the lens of innovation, customer satisfaction, or market disruption, but also as a measurable and strategic input into production functions that define bank performance. Hypothesis one is strongly confirmed, as banks that score higher on FinTech indicators also consistently outperform their peers in DEA-measured efficiency and productivity indices. Hypothesis two finds moderate but meaningful support, validating that blockchain contributes positively to efficiency, although its effects are likely to be more delayed, heterogeneous across countries, and dependent on broader institutional conditions. Hypothesis three—concerning the roles of bank-specific characteristics and macroeconomic context—is confirmed, with results demonstrating that efficiency is shaped not only by technology but also by capital adequacy, size, inflation environment, and the level of financial inclusion in the broader economy.

In terms of theoretical contributions, this study advances the integration of technological innovation theory and the resource-based view (RBV) within the financial efficiency literature. By framing FinTech and blockchain as digital capabilities that generate differential performance outcomes, this paper moves beyond traditional interpretations of efficiency focused solely on physical or financial inputs and outputs. It highlights how intangible, knowledge-intensive resources—such as algorithmic processes, platform reach, and digital customer onboarding—can now be understood as core factors of competitive advantage in banking institutions. This has strong implications for how efficiency frontiers are conceptualized and for how performance benchmarking tools like DEA can be modernized to reflect the realities of 21st-century financial systems.

Methodologically, this research introduces an integrated framework that leverages the strengths of both DEA–Malmquist analysis and dynamic panel econometric modeling. The first stage allows for the estimation of intertemporal efficiency change and technological advancement without the need to specify a functional form or production function, which is particularly useful in digital environments where outputs are not easily monetized or linear. The second stage, using System GMM, allows for rigorous hypothesis testing while accounting for endogeneity and dynamic relationships between efficiency and its digital and structural determinants. This combined approach responds to prior calls in the literature for more holistic methods that can simultaneously describe performance and explain its underlying drivers.

Practically, the study’s findings offer direct implications for bank executives, financial strategists, and digital transformation teams. For managers, the data suggest that investments in FinTech platforms are not only justifiable from a service delivery perspective but are also essential for achieving sustainable gains in efficiency and scale. Mobile transaction systems, AI-assisted credit scoring, and frictionless digital onboarding can all lead to better resource allocation and cost optimization when implemented effectively. Similarly, although blockchain investments may still carry uncertainty and require ecosystem-level maturity, early adoption in targeted functions, such as clearing and settlement, compliance automation, or interbank communication, can generate early operational benefits and prepare banks for more decentralized finance models on the horizon.

For policymakers and regulators, the implications are also significant. Regulatory clarity around digital banking licenses, blockchain protocols, data interoperability, and cybersecurity standards can act as accelerators of digital productivity in the banking sector. Moreover, public investment in infrastructure—including digital ID systems, mobile internet coverage, and inclusive FinTech access—can ensure that digital transformation also serves broader development goals, such as reducing inequality and boosting financial inclusion. Policies that encourage responsible digital lending, prevent algorithmic bias, and protect consumer data rights can ensure that efficiency gains are inclusive, ethical, and sustainable.

Despite the insights generated, the study is not without limitations. First, blockchain indicators are still evolving, and existing proxies may fail to capture depth of adoption or strategic intent behind implementation. Second, the heterogeneity of legal, cultural, and institutional environments across the countries studied introduces challenges in interpretation, even though the econometric approach seeks to control for some of these effects. Third, the study uses a mid-term panel (2015–2023), which may not fully capture long-term impacts, especially in terms of strategic IT investments or gradual changes in organizational culture. Additionally, customer-side outcomes such as trust, satisfaction, or behavioral shifts were not included, though these may interact with efficiency outcomes in important ways.

Future research can expand this agenda in several directions. Longitudinal studies with deeper datasets will be able to capture the full lifecycle of digital transformation strategies, including pre-investment baselines and post-implementation effects. Comparative studies between developed and developing economies may also reveal differentiated paths to digital efficiency, shaped by structural maturity and institutional constraints. Moreover, qualitative and mixed-methods approaches can shed light on the organizational, leadership, and cultural dimensions of digital capability building in banks, which are not easily captured through quantitative performance models alone. Lastly, new data science techniques—such as natural language processing, machine learning, or sentiment analysis—may offer novel ways to quantify and explain digital adoption trends from unstructured data sources such as reports, customer reviews, or regulatory filings.

In conclusion, this study demonstrates that digital transformation, particularly through FinTech and blockchain adoption, is emerging as a foundational driver of bank efficiency in the developing world. The evidence suggests that these technologies are not only reshaping service delivery but also fundamentally altering how banks allocate resources, manage risks, and pursue productivity. For institutions that aim to remain competitive, relevant, and resilient, embracing digital capability is not an option but a necessity. As financial systems evolve, the convergence of technology, strategy, and regulation will define the new frontier of banking efficiency—one that is more agile, inclusive, and responsive to the needs of a digitally empowered society.