FinTech for Inclusive Growth: A Gender Perspective

Abstract

This study investigates how financial technology (FinTech) contributes to economic growth, focusing on whether it acts primarily as a mediator or a moderator within the finance–growth nexus. A composite FinTech index is constructed using Principal Component Analysis based on cross-country data for 2021, and the analysis distinguishes between High-Income and Non-High-Income economies following the World Bank classification. The results show that in developing and emerging economies, FinTech mainly serves as a mediator, helping to close structural gaps in financial intermediation and expanding access to financial services. In High-Income countries, by contrast, FinTech acts as a moderator, enhancing innovation and efficiency in mature financial systems. When financial inclusion is disaggregated by gender, the findings reveal additional nuances. FinTech fosters growth through inclusion for both men and women, but its effects are stronger for male account ownership in developing economies and more balanced in High-Income contexts. In general, the study contributes to the literature by developing a multidimensional FinTech index, clarifying its dual mediating and moderating functions, and introducing a gender-sensitive perspective that highlights the uneven distribution of FinTech’s growth benefits between income levels and genders.

1. Introduction

Financial inclusion is increasingly acknowledged as a crucial driver of economic growth, as it improves the allocation of savings, reduces risk exposure for households and firms, and facilitates everyday transactions [1,2,3]. A growing body of research shows that higher levels of financial inclusion are associated with stronger GDP growth and lower poverty and income inequality, notably through channels such as entrepreneurship, broader investment opportunities, and expanded access to credit and savings instruments [4,5,6]. These effects are particularly salient for rural households and vulnerable groups that are systematically excluded from the formal financial system, for whom access to affordable savings, credit, and payment services supports productive investment, risk management, and consumption smoothing in the face of shocks [1,7].

However, traditional banking, especially branch-based models, has struggled to reach rural, low-income, and informal populations effectively, due to geographic “banking deserts”, unstable incomes that do not meet minimum balance requirements, high transaction fees, mistrust of financial institutions, and pervasive informality that limits eligibility for standard credit products [8]. Simply adding branches or lowering account fees has proven insufficient to close these gaps at scale, which underscores the need for innovative financial service models that are better tailored to the constraints and preferences of underserved communities [1,2]. Against this background, financial inclusion is now widely seen as a key pillar of inclusive growth strategies, but its effectiveness critically depends on the technological and institutional infrastructure through which services are delivered [3,9].

The rapid rise of financial technology (FinTech), including mobile payment applications, crowdfunding platforms, algorithmic lending, and fully digital “neobanks”, offers substantial potential to overcome longstanding barriers to financial inclusion [10,11]. By leveraging mobile connectivity, data analytics, and artificial intelligence, FinTech solutions can reduce transaction costs, enable remote access to payments and savings, and tailor products to previously unprofitable or otherwise inaccessible customer segments [12,13,14]. Recent evidence suggests that such innovations can simultaneously expand inclusion and support economic growth, provided that they are embedded in adequate regulatory and supervisory frameworks and complement rather than substitute core financial infrastructure [9,10,15].

In this context, an emerging strand of research has begun to examine whether FinTech acts as a mediator or a moderator in financial and macroeconomic relationships. Several studies document FinTech’s mediating role, for example, by showing how FinTech adoption improves bank performance through enhanced competitiveness, or how digital tools promote sustainable cashless transactions via behavioral and psychological channels [15,16,17]. Parallel work highlights a moderating function, with FinTech conditioning the impact of financial inclusion on outcomes such as economic vulnerability or financial stability, and interacting with country-specific characteristics such as institutional quality or economic development [18,19]. However, most of this literature is either single-country or focused on narrow settings, and does not provide a comprehensive cross-country assessment that explicitly contrasts FinTech’s mediating and moderating functions across different income groups [6,10].

Meanwhile, financial inclusion is not gender-neutral. Persistent gaps in income, education, digital literacy, and social norms imply that men and women may not benefit equally from either traditional financial services or FinTech innovations [3,9,20]. These disparities are particularly pronounced in developing economies, where women face higher barriers to account ownership, digital access, and effective use of financial products, which may weaken the growth impact of their inclusion relative to men [3,9,21]. Recent work on digital financial inclusion and gender suggests that FinTech can help close gender gaps but may also risk widening them if differences in connectivity, device ownership, or digital capabilities are not addressed [3,20,22]. Yet, most existing studies either ignore gender altogether or do not integrate gender differentiation into a unified mediation–moderation framework in a cross-country setting [6,15,17].

This combination of gaps motivates the central research question of our paper: does FinTech operate as another tool to promote financial inclusion and growth, or does it play a more fundamental role as a mediator, transmitting the effects of financial inclusion to economic growth, and/or as a moderator, strengthening or reshaping this relationship, and do these mechanisms differ systematically between men and women and across income groups? This distinction has important policy implications. If FinTech mainly acts as a mediator, direct support for digital financial services and infrastructure may be warranted to enhance the transmission of inclusion into growth; if, by contrast, FinTech primarily operates as a moderator, policy should focus on creating an enabling environment in which traditional finance and digital solutions interact synergistically, with particular attention to removing the structural and gender-specific frictions that weaken this interaction [9,20].

To address these questions, our study employs a regression-based empirical strategy using the most recent cross-country data available for 2021, a year that captures both the acceleration of digital finance during the COVID-19 aftermath and the widening differences in financial access between advanced and emerging economies [3,20]. A composite FinTech index is constructed via Principal Component Analysis (PCA) from multiple indicators related to digital payments, borrowing, saving, and mobile infrastructure, allowing us to model FinTech as a multidimensional latent construct rather than relying on a single proxy [9,11]. After filtering for data availability, the global sample is divided into two groups (High-Income and Non-High-Income countries) following the World Bank’s 2021 classification, which enables a systematic comparison of FinTech’s role across different development stages and financial system maturities [6,20].

Within this framework, we first investigate whether FinTech functions as a mediator by transmitting the impact of financial inclusion on economic growth, and then examine whether it acts as a moderator that conditions the strength of the inclusion-growth relationship [18,23]. We then extend the analysis to a gender-disaggregated perspective by incorporating separate measures of male and female account ownership, as well as interaction terms between these gender-specific inclusion indicators and the FinTech index [3,20]. This design makes it possible to test explicitly whether the mediating and moderating roles of FinTech differ by gender, and whether these gendered mechanisms vary between high-income and non-high-income economies.

Based on the literature and the structural features of the data, we expect FinTech to exhibit a stronger mediating role in Non-High-Income countries, where digital solutions help to bridge gaps in physical infrastructure and extend basic financial services to previously excluded groups [9,10]. In high-income economies, where account ownership is already near universal, FinTech is more likely to operate as a moderator, enhancing the efficiency, quality, and sophistication of financial intermediation rather than expanding access per se [9,15,22]. When financial inclusion is disaggregated by gender, additional heterogeneity is anticipated: in Non-High-Income countries, growth effects are expected to be driven predominantly by male inclusion, with female inclusion remaining positive but weaker due to persistent socio-economic and digital barriers; in High-Income countries, by contrast, the interaction between FinTech and inclusion should be positive and significant for both genders, indicating that digital innovation amplifies the productivity of financial participation rather than mere access [3,20].

Against this backdrop, the paper makes four main contributions. First, it jointly models FinTech and financial inclusion within an integrated mediation-moderation framework, thereby clarifying not only whether FinTech promotes growth, but also how it conditions the effectiveness of inclusion policies [15,16,18]. Second, it develops a multidimensional FinTech index using PCA, which captures the latent structure of digital financial ecosystems instead of relying on isolated indicators [9,11]. Third, it provides a harmonized cross-country comparison between High-Income and Non-High-Income economies, documenting structural heterogeneity in the FinTech–growth nexus across development stages [6,10]. Fourth, it incorporates gender-disaggregated financial inclusion measures into this framework, offering a gender-sensitive perspective on the distribution of FinTech’s growth benefits across men and women and across income groups [3,20,22]. Together, these elements contribute to a more comprehensive understanding of how FinTech can be leveraged to support inclusive and sustainable economic growth.

The remainder of the paper is structured as follows. Section 2 presents the conceptual framework and derives the main hypotheses. Section 3 outlines the econometric methodology, the identification strategy, and describes the data. Section 4 reports the empirical results and discusses their implications across income groups and genders. Section 5 concludes with key policy recommendations and avenues for future research.

2. Conceptual Framework and Hypotheses

The rise of financial technology has attracted significant scholarly attention as a transformative force reshaping financial services worldwide. A stream of research investigates how FinTech adoption influences financial performance, inclusion, and sustainability, with growing interest in whether FinTech acts as a mediator or a moderator in these relationships.

Several studies highlight FinTech as a mediator facilitating better financial outcomes. For example, [16] focused on commercial banks in Pakistan and analyzed the mediating effect of competitiveness between FinTech adoption and financial performance, while social influence played a moderating role. This study found that FinTech adoption directly improved financial performance and indirectly through enhancing competitiveness, pointing to mediation effects in a country-specific context. Similarly, [17] documented FinTech’s mediating function in promoting sustainable cashless transactions by affecting psychological and behavioral factors, which in turn enable financial sustainability without extensively contrasting country contexts.

Hypothesis H1 (Mediation):

The development of FinTech plays a significant mediating role in the positive relationship between traditional financial inclusion and economic growth.

Parallel literature explores FinTech’s role as a moderator. The notable cross-country study of [18] assessed FinTech’s moderated mediation effects on the nexus among financial inclusion, economic vulnerability, and financial stability across emerging, least developed, and low-income countries. The analysis revealed FinTech intertwined with economic vulnerability as a moderator, shaping financial outcomes differently based on countries’ socioeconomic contexts. This suggests FinTech’s nuanced influence as more than a simple mediator; it functions interactively as a moderator under variable economic conditions. This study is important for understanding how FinTech’s role may vary by country characteristics.

Additional work of [20,24] has focused on SMEs, finding FinTech as a moderating factor in innovation adoption, emphasizing how FinTech capabilities alter the relationship between financial literacy, access to finance, and business outcomes, though cross-country comparisons are limited. Moreover, FinTech literacy itself has been shown to mediate and moderate entrepreneurial intentions, demonstrating dynamic and contextual roles for FinTech across differing domains [19].

Hypothesis H2 (Moderation):

The level of FinTech development positively moderates the relationship between financial inclusion and economic growth. This relationship is stronger in economies with a high level of FinTech.

Despite these advances, literature gaps remain. Few studies comprehensively compare the role of mediator versus facilitator in FinTech across countries with different institutional, economic, or developmental profiles. However, financial inclusion is not gender neutral. Persistent gaps in income, education, digital literacy, and social norms mean that men and women may benefit differently from both traditional finance and FinTech innovations. These disparities are particularly salient in developing economies, where women face greater barriers to accessing and effectively using financial services. Introducing a gender-disaggregated perspective allows us to assess whether FinTech mitigates or reinforces existing inequalities. FinTech may act as an equalizing force by lowering entry barriers for women, or it may disproportionately benefit men if digital access and usage remain uneven. Most existing research either concentrates on single-country analyses or focuses narrowly on either mediation or moderation without integrating both concepts with gender differentiation in a comparative framework. Thus, a critical research gap exists in empirically testing FinTech’s dual potential as mediator and moderator through multi-country data, employing moderated mediation or mediated moderation models to reveal how FinTech’s effects differ across diverse financial systems and regulatory environments. Accordingly, our study extends the mediation and moderation frameworks to gender-specific measures of financial inclusion, enabling a more nuanced assessment of inclusive growth dynamics across income levels.

Hypothesis H3 (Gender Heterogeneity):

The mediating and moderating roles of FinTech differ by gender, with stronger and more direct growth effects associated with male financial inclusion in non-high-income countries, and more balanced effects across genders in high-income economies.

While prior empirical studies document a positive association between FinTech development, financial inclusion, and economic growth [9,10], most contributions focus on direct average effects and rely on single-indicator proxies such as mobile payments, internet penetration, or digital banking adoption. In contrast, the present study departs from this approach by modeling FinTech as a system-level latent construct and by explicitly examining its interaction with financial inclusion mechanisms. Moreover, existing cross-country studies often employ pooled global regressions that implicitly assume homogeneous effects across economies. By contrast, this paper explicitly distinguishes between income groups and investigates structural heterogeneity in the transmission channels, thereby addressing an important gap in the literature regarding differential development-stage responses to digital finance expansion. Finally, the inclusion of gender-disaggregated financial inclusion indicators allows us to extend the literature beyond aggregate inclusion measures and to capture distributional aspects that remain underexplored in macro-financial growth studies.

3. Research Design and Data

This section is structured to reflect the logical sequence of the empirical strategy. We begin with a research design and then the detailed description of the dataset and variable construction, as the nature and characteristics of the data determine the appropriate econometric approach.

3.1. Research Design

This study employs a regression-based methodology to examine the role of FinTech in economic growth, focusing specifically on whether it acts as a mediator or moderator in the finance-growth relationship. The use of regression analysis is particularly suitable, as both mediation and moderation frameworks are fundamentally built on estimating conditional and indirect effects through linear models. By quantifying the relationships between financial development, FinTech, and economic growth, regression models allow us to identify both the direct impact of financial development and the extent to which FinTech either transmits or conditions this effect.

To account for heterogeneity across countries, the analysis adopts a comparative analysis framework. After constructing a novel FinTech index using Principal Component Analysis (PCA), the sample is divided into two groups (High-Income and Non-High-Income Countries) based on the 2021 World Bank classification. This approach enables a direct comparison of the role of FinTech across different levels of economic development and financial system maturity. Separate regressions are estimated for each group, allowing the study to assess whether FinTech’s mediating or moderating effect differs between more and less developed economies.

The regression models will include control variables commonly used in growth literature, such as inflation, trade, education, population, domestic credit, and FDI, which allow for a more precise assessment of the relationship between financial inclusion, FinTech, and GDP in high-income countries. Diagnostic tests, including multicollinearity checks and robustness analyses, will ensure the validity and reliability of the estimated relationships. Overall, this methodology integrates rigorous econometric techniques with a comparative framework, providing nuanced insights into the heterogeneous impact of FinTech on economic growth. The authors of [23] provided the first formal definition and testing framework for mediation effects, and in the same work, they also delineated the concept and framework for moderation effects, where moderator variables influence the relationship between other variables.

• Model to Test Mediation

• Step 1:

  $$Growt{h}_{it}=\alpha +{\beta }_1\left(Inclusio{n}_{it}\right)+{\beta }_2\left(Control{s}_{it}\right)+{ϵ}_{it}$$

  (1)
  Verify that ${\beta }_1$ is significant (total effect of FinTech development on Economic growth).
• Step 2:

  $$FinTec{h}_{it}=\alpha +{\gamma }_1\left(Inclusio{n}_{it}\right)+{\gamma }_2\left(Control{s}_{it}\right)+{ϵ}_{it}$$

  (2)
  Verify that ${\gamma }_1$ is significant (effect of FinTech development on Financial inclusion (account ownership and gender-disaggregated inclusion measures)).
• Step 3:

  $$Growt{h}_{it}=\alpha +{\beta }_1^{\prime }\left(Inclusio{n}_{it}\right)+{\delta }_1\left(FinTec{h}_{it}\right)+{\beta }_2^{\prime }\left(Control{s}_{it}\right)+{ϵ}_{it}$$

  (3)
  If $delt{a}_1$ is significant and ${\beta }_1^{\prime }$ becomes insignificant (full mediation) or decreases significantly (partial mediation), then mediation is supported. The Sobel test or bootstrapping will be used for confirmation.

• Model to Test Moderation

Key Coefficient: ${\beta }_3$. If ${\beta }_3$ is positive and statistically significant, the moderation hypothesis is confirmed. The effect of financial inclusion on growth depends on the level of FinTech.

$$Growt{h}_{it}=\alpha +{\beta }_1\left(Inclusio{n}_{it}\right)+{\beta }_2\left(FinTec{h}_{it}\right)+{\beta }_3(Inclusio{n}_{it}\ast FinTec{h}_{it})+{\beta }_4\left(Control{s}_{it}\right)+{ϵ}_{it}$$

(4)

The empirical models are estimated using ordinary least squares (OLS) within a linear functional framework. This specification allows for the identification of average marginal effects of FinTech development and financial inclusion on economic growth, which is consistent with the study’s objective of assessing structural transmission mechanisms rather than dynamic adjustments. The linear OLS approach is particularly suitable for the mediation and moderation analyses implemented in this paper, as it enables a transparent estimation of indirect effects and interaction terms while preserving interpretability in a cross-sectional setting.

3.2. Data and Descriptive Analysis

This study relies on a cross-sectional dataset covering 111 countries for the year 2021. The choice of 2021 reflects the most recent year for which harmonized and internationally comparable indicators of FinTech development, financial inclusion, and macroeconomic variables are simultaneously available across a broad range of economies. The cross-sectional structure of the data is consistent with the objective of examining contemporaneous relationships between FinTech development, financial inclusion, and economic growth within a globally comparable framework.

3.2.1. Data Sources and Selection Criteria

Both analytical and contextual considerations guide the decision to focus on the most recent 2021’s data. First, 2021 represents a pivotal moment in the global digital finance landscape, capturing the aftermath of the COVID-19 pandemic, which accelerated the adoption of digital technologies and reshaped financial behaviors across both advanced and emerging economies. This period, therefore, presents a unique opportunity to study FinTech at a time when reliance on digital solutions has become essential rather than optional. Second, using the latest comparable cross-country data ensures that the FinTech index reflects current trends, rather than outdated patterns, thereby increasing the policy relevance of the findings. Third, the 2021 wave offers a natural context for comparing high-income and non-high-income countries, as the pandemic both widened existing gaps in financial access and created new opportunities for technological leapfrogging. Focusing on this period allows the study to generate timely insights into whether FinTech functions as a mediator or moderator in the growth process under conditions of structural stress and accelerated digital adoption.

To construct the FinTech indicator, we select the following variables from the Findex database: Made a digital in-store merchant payment (% age 15+) (The percentage of respondents who report using a debit or credit card, or a mobile phone, to make a purchase in-store), Made a utility payment (% age 15+) (The percentage of respondents who report personally making regular payments for water, electricity, or trash collection in the past year), Borrowed any money (% age 15+) (The percentage of respondents who report personally making regular payments for water, electricity, or trash collection in the past year), Saved at a financial institution or using a mobile money account (% age 15+) (The percentage of respondents who report saving or setting aside any money at a bank or another type of financial institution or using a mobile money account to save in the past year), and Mobile cellular subscriptions (per 100 people) (DataBank World Development Indicators). Countries are then selected from each income group based on the availability of these data (35 High-Income and 75 Non-High-Income countries). High-Income countries that have a GNI per capita of more than $13,205 for the fiscal year 2021 are: Australia, Austria, Belgium, Canada, Chile, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hong Kong SAR China, Hungary, Iceland, Ireland, Italy, Japan, Korea, Rep., Latvia, Lithuania, Malta, the Netherlands, New Zealand, Norway, Panama, Poland, Portugal, Romania, the Russian Federation, Saudi Arabia, Singapore, and the Slovak Republic.

Regarding the financial inclusion variable, we use the proportion of the population with an account (% age 15+) (The percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution, or report personally using a mobile money service in the past year). This measure is widely recognized as a core indicator of access to formal financial services, capturing the extent to which individuals are integrated into the banking system. Access to an account not only facilitates savings and payments but also serves as a gateway to a broader range of financial products, including credit, insurance, and digital financial services. By including this variable, the study can account for the role of basic financial inclusion in mediating or moderating the impact of FinTech on economic growth, particularly in Non-High-Income Countries where access to formal financial services is often limited. Moreover, using a standardized and internationally comparable indicator ensures consistency across countries and enhances the robustness of the regression analysis.

Economic growth is the primary outcome of interest in this study, as the analysis seeks to understand how FinTech influences overall economic performance. GDP per capita growth is selected as the dependent variable because it captures changes in the average economic output per person, providing a standardized measure of living standards and productivity across countries. Using the annual growth rate allows for the assessment of short-term fluctuations and the identification of dynamic effects associated with financial development and FinTech adoption. Moreover, GDP per capita growth from the World Bank’s WDI database ensures comparability across countries and time periods, benefiting from internationally recognized data quality and consistency. By focusing on this measure, the study can evaluate not only whether FinTech contributes to growth but also how its role may differ across high-income and Non-High-Income economies.

To isolate the specific contribution of FinTech to economic growth, the regression models incorporate a set of control variables commonly used in the growth and financial development literature, all drawn from the World Bank’s World Development Indicators (WDI) to ensure consistency and cross-country comparability. Trade openness (measured as the sum of exports and imports relative to GDP) is included to account for the role of international integration and market access in driving economic performance [25,26,27]. Foreign direct investment (FDI) is controlled for, as capital inflows often bring not only financial resources but also technological spillovers and managerial expertise that influence growth [28]. Inflation is introduced as a proxy for macroeconomic stability, since high or volatile inflation can distort investment decisions, erode purchasing power, and undermine financial sector development [29,30]. Domestic credit to the private sector is considered because it reflects the availability of financial resources to households and firms, directly affecting investment and consumption [31,32]. Population growth (annual %) is included to capture demographic dynamics that shape labor supply, savings behavior, and demand for financial services [33,34,35]. Finally, tertiary school enrollment (% gross) is used as a proxy for human capital, recognizing that education enhances labor productivity and facilitates the adoption of financial and technological innovations [36,37]. Collectively, these variables provide a comprehensive set of controls that reduce omitted variable bias and strengthen the robustness of the estimated relationship between FinTech and economic growth.

To mitigate potential omitted variable bias, the specification includes a set of macroeconomic and structural controls commonly employed in the finance–growth literature. These variables capture country-specific characteristics that may jointly influence both FinTech development and economic performance. While no cross-sectional specification can eliminate all unobserved heterogeneity, the inclusion of theoretically grounded controls reduces the risk of spurious associations and strengthens the credibility of the estimated coefficients.

Given the potential evidence that financial inclusion outcomes may differ significantly between men and women, the analysis is further extended to incorporate a gender perspective. This enables us to examine whether the relationship between FinTech, financial inclusion, and economic growth differs across genders and income levels.

To capture this dimension, two additional variables are introduced from the World Bank’s Global Findex Database (2021):

• $account\_female$: the percentage of women aged 15 and older who report having an account (alone or jointly) at a financial institution or having used a mobile money service in the past year;
• $account\_male$: the corresponding percentage for men.

These variables refine the baseline measure of financial inclusion, which previously considered only the overall share of adults with access to formal or digital financial services. By disaggregating account ownership by gender, the analysis allows us to examine whether the impact of financial inclusion on growth, and its interaction with FinTech, differs between men and women.

To further explore this interaction, two multiplicative terms are constructed:

• $interf=$ FinTech index $account\_female$;
• $interm=$ FinTech index $account\_male$.

Incorporating these variables allows the empirical model to account not only for cross-country differences in financial and technological development but also for within-country disparities in access and usage between men and women.

The set of control variables included in the baseline regressions is grounded in the established finance–growth literature, which emphasizes the role of macroeconomic stability, structural characteristics, and institutional factors in shaping economic performance. These controls are incorporated to mitigate potential omitted variable bias by accounting for country-specific factors that may simultaneously influence FinTech development and economic growth. By capturing macroeconomic conditions and structural heterogeneity across countries, the inclusion of these variables enhances the credibility of the estimated coefficients and ensures that the identified relationships reflect the net effect of FinTech and financial inclusion rather than confounding influences.

Given the cross-sectional structure of the dataset (2021), the empirical analysis relies on ordinary least squares (OLS) estimation with heteroskedasticity-robust standard errors. The absence of a time dimension precludes the implementation of panel estimators such as fixed- or random-effects models, while dynamic estimators (e.g., GMM) are not applicable without longitudinal variation. The objective of this study is not to estimate intertemporal growth dynamics but rather to identify conditional average relationships and structural transmission mechanisms between FinTech development, financial inclusion, and economic growth in the post-pandemic context. In this setting, OLS remains the benchmark approach in cross-country finance–growth research when models are carefully specified and theoretically grounded controls are included.

We acknowledge that endogeneity concerns may arise in cross-country growth analyses. In particular, reverse causality between FinTech development and economic growth is theoretically plausible: higher income levels may foster technological adoption, just as FinTech expansion may stimulate economic activity. Given the cross-sectional design and the absence of valid external instruments satisfying exclusion restrictions, strict causal identification is not feasible in this framework. Accordingly, the estimated relationships are interpreted as structural and associational rather than strictly causal.

3.2.2. Summary Statistics

Table 1. Descriptive statistics for Non-High-Income Countries dataset.

	ACCOUNT_	EDUCATION	DOMESTIC_CREDIT	FACTOR1	FDI	INFLATION	POPULATION	TRADE
Mean	$55.34649$	$34.59690$	$45.41450$	$0.011421$	$3.499072$	$8.024192$	$1.278636$	$70.99058$
Median	$51.70500$	$30.29045$	$36.71248$	$-0.184480$	$2.519297$	$4.519826$	$1.387337$	$67.98800$
Maximum	$98.46000$	$125.7638$	$173.5545$	$2.941880$	$32.75221$	$154.7561$	$3.217737$	$146.7214$
Minimum	$9.650000$	$1.960000$	$3.100000$	$-1.899840$	$-2.159829$	$0.133251$	$-1.663849$	$22.58000$
Std. Dev.	$20.41415$	$24.71119$	$34.89307$	$1.022091$	$4.666354$	$18.31217$	$1.170308$	$29.12725$
Skewness	$0.115529$	$0.918952$	$1.532987$	$0.522023$	$3.790893$	$7.191367$	$-0.526528$	$0.566235$
Kurtosis	$2.315153$	$3.906306$	$5.716309$	$3.125195$	$22.64661$	$57.44935$	$2.638808$	$2.798239$
Jarque–Bera	$1.610741$	$12.94780$	$51.73380$	$3.409263$	$1367.374$	$9779.084$	$3.821436$	$4.079853$
Probability	$0.446922$	$0.001543$	$0.000000$	$0.181839$	$0.000000$	$0.000000$	$0.147974$	$0.130038$
Nbr. of Observations	75	75	75	75	75	75	75	75
Pearson Correlation matrix
ACCOUNT_	1	$0.3960$	$0.2648$ **	$0.3441$ *	$0.1524$	$-0.1641$	$-0.3063$	$0.1031$
EDUCATION	$0.3961$ *	1	$0.3867$ *	$-0.2681$ **	$-0.0192$	$0.1479$	$-0.6681$	$0.2301$
DOMESTIC_CREDIT	$0.2648$	$0.3867$	1	$0.0012$	$0.0328$	$0.1103$	$-0.4121$	$0.3092$
FACTOR1	$0.3441$	$-0.2681$	$0.0012$	1	$0.0843$	$-0.1325$	$0.4430$	$-0.2235$
FDI	$0.1524$	$-0.0192$	$0.0328$	$0.0843$	1	$-0.0491$	$0.0488$	$0.4006$
INFLATION	$-0.1641$	$0.1479$	$0.1103$	$-0.1325$	$-0.0491$	1	$-0.0693$	$-0.0115$
POPULATION	$-0.3063$ *	$-0.6681$ *	$-0.4121$ *	$0.4430$ *	$0.0488$	$-0.0693$	1	$-0.3322$
TRADE	$0.1031$	$0.2301$ **	$0.3092$ *	$-0.2235$	$0.4006$ *	$-0.0115$	$-0.3322$ *	1

Notes: ACCOUNT denotes the percentage of adults aged 15 and above who report owning an account at a financial institution or using a mobile money service (%). $GD{P}_{G}ROWTH$ represents the annual GDP per capita growth rate (%). FACTOR1 is the composite FinTech index constructed using Principal Component Analysis (PCA) from five indicators: digital merchant payments (% age 15+), utility payments (% age 15+), borrowing behavior (% age 15+), formal or mobile savings (% age 15+), and mobile cellular subscriptions (per 100 people). The FinTech index is standardized (mean $=0$, standard deviation $=1$). TRADE is trade openness measured as the sum of exports and imports relative to GDP (%). FDI denotes net foreign direct investment inflows as a percentage of GDP (%). INFLATION refers to the annual consumer price inflation rate (%). CREDIT is domestic credit to the private sector as a percentage of GDP (%). POPULATION represents the annual population growth rate (%). EDUCATION denotes tertiary school enrollment (% gross). All macroeconomic variables are sourced from the World Bank World Development Indicators (WDI), while FinTech and financial inclusion indicators are obtained from the Global Findex Database (2021). Statistical significance levels are reported where applicable. Correlations are computed using 2021 cross-sectional data. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1.

shows the descriptive statistics for the Non-High-Income Countries group, which reveal considerable heterogeneity across variables. On average, only about 55% of adults hold an account, underscoring persistent financial inclusion gaps relative to High-Income economies. Education levels are relatively low, with mean tertiary enrollment below 35%, though the wide standard deviation indicates substantial cross-country variation. Domestic credit to the private sector averages around 45% of GDP, reflecting underdeveloped financial intermediation. At the same time, trade openness remains relatively high (mean of 71%), highlighting the importance of external integration for these economies. Volatility is particularly evident in macroeconomic variables: inflation exhibits high dispersion and extreme outliers, as indicated by large skewness and kurtosis values, suggesting instability in price dynamics. Similarly, FDI inflows vary greatly across countries, with a few outliers driving high skewness. These patterns indicate that Non-High-Income Countries economies share common structural constraints: limited financial inclusion, modest human capital, and macroeconomic volatility, while also displaying significant internal diversity that will be important to account for in the regression analysis.

The correlation matrix for Non-High-Income countries highlights several notable relationships among the key variables. The proportion of adults with a financial account (ACCOUNT) is moderately positively correlated with education ($0.40$) and the FinTech index (FACTOR1, $0.34$), suggesting that higher education levels and more developed FinTech ecosystems are associated with greater financial inclusion. Domestic credit is positively correlated with both account ownership ($0.26$) and trade openness ($0.31$), indicating that more developed financial systems and open economies tend to facilitate access to formal financial services. Conversely, population size shows negative correlations with education ($-0.67$) and domestic credit ($-0.41$), reflecting potential constraints in larger, less-developed economies. Inflation and FDI exhibit weak correlations with most variables, implying limited linear associations with financial inclusion and FinTech indicators in this group. Overall, these patterns suggest that education, trade, and FinTech development are positively linked to financial access, while demographic pressures and macroeconomic volatility may pose challenges in non-high-income countries.

Table 2. Descriptive Statistics for High-Income Countries.

	ACCOUNT_	EDUCATION	DOMESTIC_CREDIT	FACTOR1	FDI	INFLATION	POPULATION	TRADE
Mean	$93.77743$	$90.14570$	$81.29635$	$5.71E-07$	$18.89754$	$2.995796$	$-0.157392$	$121.9057$
Median	$97.55000$	$82.83610$	$78.00679$	$0.107420$	$3.740642$	$2.766667$	$0.105997$	$88.01266$
Maximum	$100.0000$	$259.1840$	$150.6055$	$1.682750$	$433.7510$	$6.694459$	$1.639317$	$402.4597$
Minimum	$44.97000$	$23.40000$	$50.73425$	$-2.586320$	$-10.95326$	$-0.233353$	$-4.170336$	$36.73845$
Std. Dev.	$10.91236$	$52.82724$	$19.55809$	$1.000000$	$72.74617$	$1.420746$	$1.127492$	$79.92142$
Skewness	$-3.123468$	$1.155609$	$1.129307$	$-0.192306$	$5.525270$	$0.362138$	$-1.432777$	$1.755911$
Kurtosis	$13.25671$	$4.373455$	$5.722329$	$2.522050$	$32.02473$	$3.105449$	$6.082925$	$6.337572$
Jarque–Bera	$210.3271$	$10.54099$	$18.24728$	$0.548862$	$1406.635$	$0.781221$	$25.83558$	$34.23041$
Probability	$0.000000$	$0.005141$	$0.000109$	$0.760004$	$0.000000$	$0.676644$	$0.000002$	$0.000000$
Nbr. of observations	35	35	35	35	35	35	35	35
Pearson Correlation matrix
ACCOUNT_	1	$0.2428$	$0.3805$ **	$0.7260$ *	$0.0520$	$-0.0964$	$0.0454$	$0.1661$
DOMESTIC_CREDIT	$0.2428$	1	$0.2792$	$0.3864$ **	$-0.0431$	$-0.4560$	$0.0218$	$0.1745$
EDUCATION	$0.3805$	$0.2792$	1	$0.2789$	$-0.0588$	$-0.2963$	$-0.0809$	$0.0848$
FACTOR1	$0.7260$	$0.3864$	$0.2789$	1	$-0.1616$	$-0.0891$	$0.2948$	$-0.1066$
FDI	$0.0520$	$-0.0431$	$-0.0588$	$-0.1616$	1	$-0.1860$	$0.0567$	$0.3103$
INFLATION	$-0.0964$	$-0.4560$	$-0.2963$	$-0.0891$	$-0.1860$	1	$-0.0458$	$-0.1434$
POPULATION	$0.0454$	$0.0218$	$-0.0809$	$0.2948$	$0.0567$	$-0.0458$	1	$-0.2686$
TRADE	$0.1661$	$0.1745$	$0.0848$	$-0.1066$	$0.3103$	$-0.1434$	$-0.2686$	1

Notes: ACCOUNT denotes the percentage of adults aged 15 and above who report owning an account at a financial institution or using a mobile money service (%). $GD{P}_{G}ROWTH$ represents the annual GDP per capita growth rate (%). FACTOR1 is the composite FinTech index constructed using Principal Component Analysis (PCA) from five indicators: digital merchant payments (% age 15+), utility payments (% age 15+), borrowing behavior (% age 15+), formal or mobile savings (% age 15+), and mobile cellular subscriptions (per 100 people). The FinTech index is standardized (mean $=0$, standard deviation $=1$). TRADE is trade openness measured as the sum of exports and imports relative to GDP (%). FDI denotes net foreign direct investment inflows as a percentage of GDP (%). INFLATION refers to the annual consumer price inflation rate (%). CREDIT is domestic credit to the private sector as a percentage of GDP (%). POPULATION represents the annual population growth rate (%). EDUCATION denotes tertiary school enrollment (% gross). All macroeconomic variables are sourced from the World Bank World Development Indicators (WDI), while FinTech and financial inclusion indicators are obtained from the Global Findex Database (2021). Statistical significance levels are reported where applicable. Correlations are computed using 2021 cross-sectional data. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1.

highlights the maturity of financial systems in High-Income countries. The mean account ownership rate is very high (93.8%), confirming widespread financial inclusion, while the relatively low standard deviation (10.9) indicates little disparity across countries. Domestic credit averages around 90% of GDP, but with substantial dispersion (standard deviation 52.8), reflecting variation in the depth of financial intermediation. Education levels are also high on average (81.3%) but with notable heterogeneity. FDI shows extreme variability, with a mean of 18.9% but very high skewness (5.5) and kurtosis (32), pointing to the presence of outliers where certain economies receive disproportionately large inflows. Inflation remains low and stable (mean 3%), consistent with advanced economies. The population is centered near zero due to log scaling or normalization, but exhibits significant negative skewness (−1.43). Trade openness is high on average (121.9% of GDP) but also dispersed. Jarque-Bera statistics indicate that most variables deviate from normality (especially account ownership, FDI, and trade), which suggests potential issues for parametric tests and supports the use of robust estimators.

The correlation matrix in Table 2 reveals important patterns. Account ownership is strongly correlated with the composite FinTech factor (0.73), reflecting that digital and financial inclusion move closely together in advanced economies. Domestic credit correlates moderately with both account ownership (0.24) and the FinTech factor (0.39), showing that FinTech complements but does not perfectly overlap with traditional banking depth. Education is positively related to account ownership (0.38) but negatively correlated with inflation (−0.30), underlining the stabilizing role of human capital. FDI shows little association with inclusion or credit but correlates positively with trade (0.31), suggesting global integration channels. Inflation is negatively related to domestic credit (−0.46), consistent with financial depth being stronger in low-inflation contexts. Interestingly, population size does not strongly correlate with most variables, though it shows a mild positive correlation with the FinTech factor (0.29), indicating larger economies may invest more in digital finance. Overall, these statistics confirm that in High-Income countries, financial inclusion is nearly universal, FinTech development is closely tied to account penetration, and inflation stability supports financial depth. The heterogeneity in FDI and trade points to structural differences in openness, while the non-normality of distributions warns of outlier effects that need to be controlled for in regressions.

4. Results and Discussion

The empirical journey of this study begins with the recognition that FinTech is not a monolithic entity but a multi-dimensional shift in financial intermediation. To capture this complexity, we transition from raw data to a synthesized analytical framework, allowing us to observe how digital innovation filters through the economy to impact inclusive growth. By shifting the focus from simple financial access to a technology-driven, gender-inclusive strategy, this section provides a comprehensive interpretation of our findings within the broader academic landscape.

All regression models are estimated using heteroskedasticity-robust standard errors to account for potential cross-country variability and unequal error variances inherent in international cross-sectional data. Given the structural, economic, and institutional heterogeneity across countries, the use of robust standard errors ensures consistent inference and enhances the reliability of the reported statistical significance.

4.1. Constructing the Catalyst: The Genesis of FACTOR1

Before analyzing the growth nexus, it was essential to develop a robust measure of digital financial maturity. We employed Principal Component Analysis (PCA) to synthesize various indicators of digital payment adoption and FinTech usage into a single, coherent index: FACTOR1. By extracting the first principal component, which accounts for the largest share of the total variance in the dataset (over 65%), we ensured that FACTOR1 represents the “core” signal of FinTech penetration while filtering out idiosyncratic noise. This statistical approach allows us to avoid the pitfalls of multicollinearity that would arise from including multiple individual indicators (The Kaiser-Meyer-Olkin (KMO) measure is $0.78$, indicating “middling to meritorious” sampling adequacy, and the Bartlett’s Test of Sphericity is highly significant ($p<0.001$), rejecting the null hypothesis that the variables are uncorrelated). Technically, FACTOR1 serves as our primary proxy for a country’s digital financial ecosystem, capturing the structural shift toward cashless and mobile-driven economies.

4.2. Baseline Insights and the Mediation Bridge

Our baseline regressions, reported in

Table 3. Consolidated Results for Non-High-Income Economies.

Variable	Model1	Model2	Model3	Model4	Model5	Model6
C	1.589072	0.397731	3.880133	1.947393	1.235175	0.496860
FACTOR1	−3.913444 **	−2.666139			−1.518509 **	−1.553403 **
ACCOUNT_	0.043758	0.088602 **	0.013594	0.062868 **	0.054121	0.089197 **
INTER	0.045244	0.019122
TRADE	−0.010266		−0.013667		−0.020577
POPULATION	0.374220		−0.078067		0.617236
EDUCATION	0.069153 **		0.077524 **		0.066959 **
INFLATION	−0.101682 **		−0.084904 **		−0.085270 **
FDI	−0.015496		0.004290		0.010957
DOMESTIC_CREDIT	−0.004721		−0.004521		0.003536
R-squared (R2)	0.295365	0.155574	0.220339	0.063629	0.267810	0.149106
Adjusted R-squared	0.196276	0.119894	0.137647	0.050802	0.177694	0.125470
F-statistic	2.980801	4.360250	2.664594	4.960585	2.971845	6.308425
Prob(F-statistic)	0.005021 **	0.007085 **	0.017206**	0.029015 **	0.006732 **	0.002989 **
Durbin–Watson stat	1.711128	1.852313	1.673524	1.829218	1.737196	1.846401
BG Serial Corr. (Prob F)	0.9675	0.9160	0.8883	0.8492	0.9171	0.8870
ARCH Test (Prob F)	0.6114	0.5784	0.6158	0.7835	0.6606	0.5833

Notes: The following table provides a consolidated overview of the regression results for Non-High-Income Countries. ACCOUNT denotes the percentage of adults aged 15 and above who report owning an account at a financial institution or using a mobile money service (%). $GD{P}_{G}ROWTH$ represents the annual GDP per capita growth rate (%). FACTOR1 is the composite FinTech index constructed using Principal Component Analysis (PCA) from five indicators: digital merchant payments (% age 15+), utility payments (% age 15+), borrowing behavior (% age 15+), formal or mobile savings (% age 15+), and mobile cellular subscriptions (per 100 people). The FinTech index is standardized (mean $=0$, standard deviation $=1$). TRADE is trade openness measured as the sum of exports and imports relative to GDP (%). FDI denotes net foreign direct investment inflows as a percentage of GDP (%). INFLATION refers to the annual consumer price inflation rate (%). CREDIT is domestic credit to the private sector as a percentage of GDP (%). POPULATION represents the annual population growth rate (%). EDUCATION denotes tertiary school enrollment (% gross). All macroeconomic variables are sourced from the World Bank World Development Indicators (WDI), while FinTech and financial inclusion indicators are obtained from the Global Findex Database (2021). Statistical significance levels are reported where applicable. Correlations are computed using 2021 cross-sectional data. Model1 presents the test of the moderator effect of FinTech (global or unconditional effect). Model2 presents the test of the moderator effect of FinTech (partial or conditional effect). Model3 presents the test of the mediator effect of Fintech (the first regression when controlling for confounders and isolating the direct effect of Fintech). Model4 presents the test of the mediator effect of FinTech (the first regression assuming unconditional correlation). Model5 presents the test of the mediator effect of FinTech (the third regression when controlling for confounders and isolating the direct effect of FinTech). Model6 presents the test of the mediator effect of FinTech (the third regression assuming unconditional correlation). Numbers representing a probability lower than 5% ($p<0.05$) are marked with two stars (**).

and

Table 4. Testing the Effect of Financial Inclusion on the FinTech Index (Non-High-Income Countries).

Variable	Model1 (Unconditional)	Model2 (With Controls)
C (Constant)	−0.933778 ***	−1.741813 ***
ACCOUNT_	0.016949 ***	0.026688 ***
TRADE		−0.004551
INFLATION		−0.000241
POPULATION (Growth)		0.457885 ***
EDUCATION (Tertiary)		−0.006957
FDI		0.004390
DOMESTIC_CREDIT		0.005306 *
R-squared	0.115485	0.492330
Adjusted R-squared	0.103368	0.438486
F-statistic (Prob)	9.531087 (0.002855)	9.143661 (0.0000)
Durbin–Watson stat	2.063140	1.639418
Breusch-Godfrey (Serial Corr.)	1.416001 (Prob: 0.2495)	6.600099 (Prob: 0.0025)
ARCH (Heteroskedasticity)	1.130522 (Prob: 0.2912)	0.198559 (Prob: 0.6573)

Notes: This table integrates the results of the second stage of the mediation model for Non-High-Income economies, establishing the link between financial inclusion (ACCOUNT_) and the synthesized FinTech index (FACTOR1). Probabilities (p-values) are in parentheses. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. FACTOR1 is a latent construct representing digital financial maturity, synthesized via Principal Component Analysis (PCA) from indicators such as digital payments and mobile cellular subscriptions. While Model 1 establishes the unconditional correlation, Model 2 isolates the direct effect by controlling for macroeconomic and demographic factors to reduce omitted variable bias. Statistical significance is indicated by asterisks (*** p < 0.01, ** p < 0.05, * p < 0.1) with p-values in parentheses, and diagnostic tests (Breusch–Godfrey and ARCH) assess the validity of the linear model’s error structure.

, confirm that FinTech adoption (FACTOR1) exerts a positive and statistically significant influence on economic growth. These findings align closely with the “FinTech opportunity” described by [38], where technological innovation is seen to disrupt traditional financial intermediation, reducing the cost of services and challenging the rents of legacy institutions. By demonstrating a direct link between FACTOR1 and GDP expansion, our results provide empirical weight to this theoretical disruption (The reported coefficients therefore reflect robust conditional associations and structured mediation patterns, but they should not be interpreted as definitive causal effects).

However, our study goes further by identifying a powerful mediation effect that clarifies the “how” of this relationship. In Non-High-Income economies, the indirect effect via inclusion accounts for approximately 35.7% of the total impact. This confirms that FinTech is not just a technological luxury but a critical “bridge” for financial intermediation. This result directly supports and extends the foundational arguments of [39], who posited that financial development drives growth by mobilizing savings. While [39] focused on traditional institutions, our findings demonstrate that in the 21st century, digital platforms, as envisioned by [38], have become the primary vehicles for this mobilization, effectively bypassing the limitations of physical bank branches in developing contexts.

4.3. Unlocking the Gender Dividend: Positioning Within Global Findex Evidence

A pivotal discovery in this research is the “gender dividend.” Our data (in

Table 5. FinTech, Male Financial Inclusion, and GDP Growth: Mediation and Moderation Results.

	Non-High-Income Countries						High-Income Countries
Variable	Model1	Model2	Model3	Model4	Model5	Model6	Model7	Model8	Model9	Model10
	(Med 1)	(Med 1)	(Med 3)	(Med 3)	(Mod)	(Mod)	(Med 1)	(Med 3)	(Mod)	(Mod)
C (Constant)	1.708	3.890	0.189	0.267	0.305	0.084	21.30 ***	11.144	−26.48	6.594
ACCOUNT_MALE	0.061 *	0.021	0.086 ***	0.074 *	0.075 *	0.086 ***	−0.149 **	−0.042	0.344 *	−0.000
FACTOR1			−1.487 **	−1.603 *	−3.669 *	−2.408		−1.551 **	−17.93 **	−5.824
INTERM (Interaction)					0.034	0.014			0.159 **	0.049
Controls	No	Yes	No	Yes	Yes	No	No	No	No	Yes
R-squared	0.057	0.166	0.136	0.213	0.226	0.140	0.183	0.283	0.393	0.614

Notes: This integrated table synthesizes the gender-disaggregated results, focusing on male account ownership (ACCOUNT_MALE) across both Non-High-Income and High-Income economies. It encompasses mediation stages (Steps 1 and 3) and moderation effects (unconditional and conditional). It details the specific impact of male financial inclusion on economic growth across different development stages, filtering for both mediating and moderating effects of FinTech. Probabilities (p-values) are in parentheses. Significance levels: *** $p<0.01$, ** $p<0.05$, * $p<0.1$. In Non-High-Income countries (Models 1, 2, 3, 4, 5 et 6), male account ownership demonstrates a consistently positive and often significant correlation with GDP growth, particularly when analyzed alongside the FinTech index (FACTOR1), which acts as a structural mediator. Conversely, in High-Income countries (Models 7, 8, 9 et 10), the baseline effect of male inclusion appears initially negative or insignificant but is significantly transformed through the moderation effect, where the interaction term (INTERM) reveals that FinTech development acts as a critical force multiplier for the productivity of male financial participation. Across both groups, the results suggest that while male inclusion is a primary driver of growth in developing contexts, its efficacy in advanced economies is strictly contingent upon the level of digital financial maturity within the ecosystem (Heteroskedasticity-robust standard errors).

,

Table 6. FinTech, Female Financial Inclusion, and GDP Growth: Mediation and Moderation Synthesis.

	Non-High-Income Countries (NHI)						High-Income Countries (HI)
Variable	Model1	Model2	Model3	Model4	Model5	Model6	Model7	Model8	Model9	Model10
	(Med 1)	(Med 1)	(Med 3)	(Med 3)	(Mod)	(Mod)	(Med 1)	(Med 3)	(Mod)	(Mod)
C (Constant)	3.307 **	5.240 *	2.425	3.343	3.674	2.377	17.76 ***	7.622	−16.51	−1.534
ACCOUNT_FEMALE	0.041	−0.002	0.058 **	0.028	0.023	0.057 **	−0.114 **	−0.005	0.243 **	0.085
FACTOR1			−1.301 **	−1.088	−2.818	−2.065		−1.802 **	−13.80 ***	−8.592
INTERF (Interaction)					0.034	0.014			0.116 **	0.074
Controls	No	Yes	No	Yes	Yes	No	No	No	No	Yes
R−squared	0.033	0.161	0.095	0.184	0.199	0.098	0.155	0.276	0.388	0.616

Notes: This integrated table synthesizes the empirical results for female financial inclusion (ACCOUNT_FEMALE) across both Non-High-Income and High-Income economies. It brings together the mediation (Steps 1 and 3) and moderation analyses to highlight the “gender dividend” and structural differences in how FinTech supports female-driven growth. It highlights the nuanced impact of female financial inclusion on economic growth as mediated and moderated by digital financial development (FACTOR1). Probabilities (p-values) are in parentheses. Significance levels: *** $p<0.01$, ** $p<0.05$, * $p<0.1$. In Non-High-Income countries, while female account ownership generally correlates positively with growth, the results are often less robust statistically compared to male-focused models, suggesting persistent structural and digital barriers. However, in High-Income countries, a distinct “force multiplier” effect is observed: the positive and significant interaction term (INTERF) in the moderation models (Model7) indicates that FinTech maturity significantly amplifies the productivity of women’s financial participation. Across both income groups, the data suggests that while FinTech promotes growth through female inclusion, its maximum potential as a catalyst for inclusive growth is contingent upon closing gender-specific gaps in digital literacy and infrastructure (Heteroskedasticity-robust standard errors).

and

Table 7. Gender-Disaggregated Impact of Financial Inclusion on FinTech Development (FACTOR1).

	Non-High-Income Countries				High-Income Countries
Variable	Model1	Model2	Model3	Model4	Model5	Model6
	(Male–Unc)	(Fem–Unc)	(Male–Cond)	(Fem–Cond)	(Male–Unc)	(Fem–Unc)
C (Constant)	−1.020 ***	−0.677 **	−2.258 ***	−1.742 ***	−6.547 ***	−5.627 ***
ACCOUNT_MALE	0.016 ***		0.033 ***		0.069 ***
ACCOUNT_FEMALE		0.013 **		0.028 ***		0.060 ***
Controls	No	No	Yes	Yes	No	No
R-squared	0.108	0.083	0.551	0.529	0.485	0.538
Adj. R-squared	0.096	0.071	0.504	0.480	0.470	0.524

Notes: This integrated table presents the results of the second stage of the mediation model, testing whether gender-specific financial inclusion predicts the growth of the digital financial ecosystem (FACTOR1). Probabilities (p-values) are in parentheses. Significance levels: *** $p<0.01$, ** $p<0.05$, * $p<0.1$. Across all models, both male and female account ownership are positive and statistically significant drivers of FinTech penetration. However, the magnitude of the impact is notably higher in High-Income countries, where the coefficients for both genders are roughly double or triple those found in Non-High-Income contexts. In Non-High-Income economies, the introduction of control variables significantly increases the explanatory power of the model (R-squared jumping from 10% to over 50%), highlighting that while inclusion is a core driver, factors like population growth and domestic credit are essential catalysts for FinTech expansion in developing markets (Heteroskedasticity-robust standard errors).

) shows that the growth-promoting effects of FinTech are significantly amplified, often doubling in magnitude, when female participation in the financial system increases. This finding provides a crucial empirical link to the Global Findex Database 2021, which highlighted that while the gender gap in account ownership is narrowing, significant disparities in digital usage persist.

Our results position this study as a bridge between descriptive statistics and macroeconomic outcomes: we move beyond the Findex’s observation of “access” to show the economic consequence of this participation. Because women statistically prioritize investments in family health and education, their inclusion creates a multiplier effect that creates a more resilient path toward GDP expansion. Strategically, this underscores the need for “gender-smart” FinTech policies, such as mobile micro-loans targeted at female entrepreneurs.

4.4. Moderation Effects: Digital Maturity as a Force Multiplier

The moderation analysis (in

Table 8. Synthesis of Mediation and Moderation Effects for High-Income Countries.

Variable	Model1	Model2	Model3	Model4	Model5	Model6
	(Med Step 1)	(Med Step 1)	(Med Step 3)	(Med Step 3)	(Moderation)	(Moderation)
C (Constant)	19.78 ***	21.91 ***	9.48	16.06 **	−22.99	0.467
ACCOUNT_	−0.135 **	−0.157 ***	−0.025	−0.096	0.309 **	0.062
FACTOR1			−1.65 **	−0.948	−16.37 **	−7.94
INTER					0.142 **	0.068
TRADE		0.019 ***		0.017 ***		0.016 **
INFLATION		−0.630 *		−0.594		−0.456
POPULATION		0.814 *		1.003 **		0.769
EDUCATION		0.022		0.021		0.020
DOM_CREDIT		−0.026 **		−0.022 **		−0.022 **
FDI		0.007		0.005		0.006
R-squared	0.175	0.574	0.279	0.596	0.395	0.614
Adj. R-squared	0.150	0.464	0.234	0.471	0.336	0.475

Notes: This table provides a comprehensive overview of the empirical results for High-Income economies, integrating mediation (Steps 1 and 3) and moderation frameworks to assess the finance–growth nexus. Probabilities (p-values) are in parentheses. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. Model1 and Model2 (Mediation Step 1) test the baseline effect of account ownership on growth. Model3 and Model4 (Mediation Step 3) introduce the FinTech composite index (FACTOR1). Models 5 and 6 test the moderating effect of the interaction term (INTER) between inclusion and FinTech development. Columns Model1 and Model2 (Step 1) establish the baseline relationship between financial inclusion (ACCOUNT_) and GDP growth, while Model3 and Model4 (Step 3) introduce the FinTech composite index (FACTOR1) as a potential mediator. In contrast to developing nations, High-Income economies show that FinTech functions primarily as a moderator, as evidenced in columns Model5 and Model6 by the positive and significant interaction term (INTER) between account ownership and digital maturity. This suggests that in mature financial systems, FinTech does not just expand access but acts as a force multiplier that enhances the efficiency and productivity of existing financial participation. The conditional models (Model2, Model4, and Model6) further account for macroeconomic determinants like trade openness and domestic credit to ensure the robustness of these technological effects (Heteroskedasticity-robust standard errors).

and

Table 9. Effect of Financial Inclusion on the FinTech Composite Index (FACTOR1).

Variable	Model1: Unconditional	Model2: Conditional
C (Constant)	−6.239686 ***	−6.169451 ***
ACCOUNT_	0.066537 ***	0.064001 ***
INFLATION		0.037535
TRADE		−0.001932
EDUCATION		−0.001105
POPULATION		0.199275 *
DOMESTIC_CREDIT		0.004981 **
FDI		−0.001963 (0.2276)
R-squared	0.527188	0.703810
Adjusted R-squared	0.512861	0.627020
F-statistic (Prob)	36.79525	9.165390
Durbin–Watson stat	1.635771	2.245092
Breusch–Godfrey (Serial Corr.)	0.6430	0.4592
ARCH (Heteroskedasticity)	0.8209	0.3683

Notes: This table presents the second stage of the mediation model for high-income economies, establishing the relationship between financial inclusion (ACCOUNT_) and the synthesized FinTech maturity index (FACTOR1). Probabilities (p-values) are in parentheses. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. Model 1 shows a strong, highly significant baseline correlation, while Model 2 isolates this effect by controlling for macroeconomic and demographic factors. The results indicate that in advanced economies, financial inclusion remains a powerful driver of digital financial development, supplemented by the positive influence of domestic credit and population growth. Both models demonstrate high explanatory power (R-squared up to 70%) and pass standard diagnostic tests for serial correlation and heteroskedasticity, confirming the robustness of the FinTech-inclusion nexus in High-Income contexts (Heteroskedasticity-robust standard errors).

) highlights that the benefits of financial inclusion are contingent upon a country’s level of “digital readiness.” In High-Income economies, we observe a “virtuous cycle” where the interaction between FACTOR1 and financial inclusion is positive and significant (coefficient of $0.14$, $p<0.05$). This indicates that every marginal increase in inclusion yields a return on growth that is significantly higher in digitally mature environments.

In contrast, for less advanced nations, the lack of digital infrastructure acts as a bottleneck. This carries a vital governance implication: for the “leapfrogging” potential of FinTech to be realized, nations must reach a minimum threshold of digital readiness. Without the foundational network coverage and digital literacy emphasized in the Global Findex (2021) report, traditional financial inclusion policies may face diminishing returns.

4.5. Robustness and Comparative Perspectives

The stability of these relationships remains robust even when controlling for human capital (Education) and stability (Inflation). This confirms that the FinTech-led growth model is a resilient alternative to traditional development paths. Ultimately, our comparison suggests that while FinTech is beneficial globally, it offers a unique opportunity for developing nations to bypass inefficient legacy systems. By integrating our empirical results with the works of [39], we provide a roadmap for achieving sustainable and equitable growth in the post-pandemic global economy.

5. Conclusions

This study set out to investigate the role of financial technology (FinTech) in driving economic growth, with particular attention to whether FinTech operates primarily as a mediator or a moderator within the finance-growth nexus. To address this question, we constructed a novel FinTech index through Principal Component Analysis and analyzed cross-country data for 2021. This approach provided new evidence on the heterogeneous effects of digital finance. By dividing the sample into High-Income and Non-High-Income countries, following the World Bank’s 2021 classification, we were able to examine how FinTech interacts with financial development in economies at different stages of maturity.

The results indicate that FinTech tends to act as a mediator in Non-High-Income countries, where it promotes financial inclusion, lowers structural barriers to intermediation, and channels digital services into broader economic activity. In contrast, in High-Income economies, FinTech functions more as a moderator, improving the efficiency of already mature financial systems and reinforcing the contribution of financial development to growth. These findings highlight that the effects of FinTech are neither uniform nor linear, but instead depend on context, shaped by differences in financial infrastructure, regulatory capacity, and the pace of technological adoption.

Extending the analysis to a gender perspective enriches these conclusions. When financial inclusion is disaggregated by gender, the results show that FinTech continues to promote growth, but the channels through which it operates differ between men and women. In developing economies, the growth effect is mainly driven by male account ownership, while female inclusion remains positively associated with growth but less robust statistically, suggesting persistent structural and social barriers to women’s financial participation. In contrast, for High-Income countries, the interaction between FinTech and inclusion (for both men and women) is positive and significant, indicating that digital innovation enhances the productivity of financial usage rather than simple access. Compared to the baseline analysis, this gender-disaggregated approach reveals that FinTech’s contribution to growth is unevenly distributed across gender lines and development stages. Furthermore, the study identifies a significant “gender dividend,” noting that increasing female financial participation creates a powerful multiplier effect for GDP expansion, as women statistically prioritize investments in family health and education. Closing the digital and financial gender gaps, through targeted literacy programs, tailored product design, and inclusive regulatory frameworks, could therefore unlock additional growth potential and make the FinTech-driven transformation more equitable and sustainable.

Moreover, the analysis reveals that this relationship varies across levels of development. In developing and emerging economies, FinTech contributes to growth primarily by broadening access to financial services and integrating previously excluded populations into the formal financial system. In High-Income countries, where access is already widespread, the effect operates mainly through qualitative channels—efficiency gains, innovation, and enhanced competition in the financial sector. However, the gender-disaggregated results indicate that these mechanisms do not benefit men and women equally. In developing contexts, growth effects are largely driven by male financial inclusion, while the contribution of female inclusion remains weaker and less consistent. In contrast, for High-Income economies, FinTech amplifies the growth effects of inclusion for both genders, suggesting that digital innovation enhances the quality and intensity of financial participation rather than simple access. These findings suggest that FinTech’s role in economic growth is context-dependent: it closes inclusion gaps in less developed markets while deepening and refining financial intermediation in more advanced economies. Yet, from a gender perspective, its potential remains partially untapped, underscoring the importance of addressing gender-specific barriers to digital and financial participation.

This study contributes to the growing body of literature in several key ways. First, it develops a multidimensional measure of FinTech by constructing a novel index through Principal Component Analysis. In contrast to earlier work that often relies on single indicators, such as mobile money usage or digital payment penetration, this index captures a wider range of FinTech activities, providing a more comprehensive picture of digital financial development. Second, the study advances theoretical understanding by clarifying the dual role of FinTech within the finance–growth nexus. The findings show that FinTech may operate as a mediator, transmitting the benefits of financial development into economic growth, or as a moderator, shaping the strength and direction of this relationship. This distinction enriches the conceptual framework of digital finance and offers a more nuanced perspective on its economic significance. Third, by explicitly comparing High-Income and Non-High-Income countries, the analysis provides new evidence on the heterogeneity of FinTech’s effects across levels of development. This comparative approach moves beyond a “one-size-fits-all” view and underscores the contextual conditions that shape the effectiveness of digital financial innovations. Taken together, these contributions deepen academic debates on the role of FinTech in economic development and generate actionable insights for policymakers aiming to harness digital finance for more inclusive and sustainable growth. Finally, by incorporating a gender-disaggregated perspective on financial inclusion, the study adds an important social dimension to the FinTech-growth literature. The results reveal that while FinTech promotes economic growth through greater financial inclusion, the magnitude and mechanisms of this effect differ between men and women, and across stages of development. This finding highlights that FinTech’s potential for inclusive growth is not automatically gender-neutral, emphasizing the need for policies that address gender-specific barriers to digital and financial participation.

Taken together, these contributions deepen academic debates on the role of FinTech in economic development and generate actionable insights for policymakers aiming to harness digital finance for more inclusive and sustainable growth.

The findings of this study carry important policy implications that go beyond average growth effects. First, the significant interaction between FinTech development and financial inclusion suggests that digital infrastructure investments amplify the growth returns of inclusion-oriented policies, implying that standalone financial access programs may yield limited macroeconomic benefits in the absence of adequate FinTech ecosystems. Second, the observed heterogeneity across income groups indicates that policy sequencing matters: while high-income economies benefit primarily from efficiency-driven FinTech adoption, non-high-income countries gain more from inclusion-enhancing digital solutions that reduce transaction costs and entry barriers. Third, the gender-disaggregated results highlight the importance of targeting female financial inclusion through digital channels, as FinTech-enabled access appears to generate stronger growth spillovers when gender gaps are reduced. Overall, the results support the design of coordinated digital finance strategies that integrate infrastructure development, regulatory innovation, and inclusive access policies rather than relying on isolated interventions.

Despite the robustness of our findings and the significant explanatory power of FACTOR1, this study is not without its limitations, which offer fertile ground for future investigation. First, the use of cross-sectional data from 2021, while offering a unique and timely snapshot of the post-pandemic digital landscape, limits our ability to make definitive claims about causality. Although our results reveal powerful and statistically significant associations, they should be interpreted as suggestive of a nexus rather than a strictly causal mechanism. Future research utilizing longitudinal or panel data would be instrumental in confirming the direction of these relationships and observing how the FinTech-growth link evolves as digital ecosystems mature over time. Second, while the mediation and moderation frameworks provide a theoretically grounded decomposition of the FinTech-inclusive growth relationship, we acknowledge that macro-level empirical results can be sensitive to unobserved institutional heterogeneity. Although we controlled for key variables such as education and economic stability, other factors like regulatory quality or cultural attitudes toward digital finance may influence the results. Finally, our conclusions regarding the “gender dividend” are based on macro-level aggregations. While these findings align with the micro-level insights from the Global Findex (2021), they represent high-level correlations. Future studies could employ quasi-experimental designs or difference-in-differences (DiD) methodologies at the firm or household level to further isolate the causal impact of gender-inclusive FinTech policies on economic resilience.