Financial Innovation and Economic Growth: An ARDL-Based Analysis for Albania

This study examines the nexus between financial innovation and economic growth in Albania over the period 2000–2023. Notably, this is the first study to explore this relationship in the context of Albania. Employing the autoregressive distributed lag (ARDL) bounds testing approach, the analysis provides empirical evidence of a stable long-term link between financial innovation and economic performance. Different forms of financial innovation generate heterogeneous effects in both the short and long run. Furthermore, Granger causality tests reveal a bidirectional and dynamic relationship, indicating that financial innovation and economic growth mutually influence each other. The findings provide indicative evidence to inform policymakers, suggesting that strategic promotion of financial innovations can support sustainable economic growth while adapting to evolving financial and macroeconomic conditions. 

Introduction

Financial innovation has increasingly attracted attention due to its potential to advance the efficiency and development of financial systems. Sustainable financial innovations strengthen the efficiency of financial systems, thereby fostering broader economic development and social prosperity (Błach, 2011). By enhancing financial inclusion, streamlining international trade, facilitating remittance flows, and improving overall financial performance, financial innovation functions as a critical engine of growth in emerging economies.

Empirical evidence consistently highlights the strong link between financial innovation and economic growth (Baraet al., 2016b). Innovations range from new financial instruments to novel business models and improved record-keeping systems, all of which enhance the efficiency of financial institutions and translate into broader economic performance (Laevenet al., 2015; Saqib, 2015). Beyond improving financial efficiency, these innovations support capital accumulation, foster industrial and technological development, and strengthen the resilience of the financial sector, collectively promoting sustained economic growth (Chou & Chin, 2011). Understanding these mechanisms is crucial, particularly in emerging economies where contextual factors may either amplify or constrain the impact of financial innovation.

Despite this potential, Western Balkan countries continue to lag behind their Central and Eastern European counterparts in terms of National Innovation System (NIS) capabilities and efficiency, which limits their capacity for sustained growth. Low levels of financial and technological innovation impede productivity, deter investment, and maintain the development gap with the EU. Evidence from the Global Innovation Index (GII) and European Innovation Scoreboard (EIS) underscores this persistent lag and emphasizes the need for targeted policy interventions to strengthen innovation ecosystems (Fedajevet al., 2024). Within this regional context, Albania represents a relevant setting for analyzing the relationship between financial innovation and economic growth.

Albania is rapidly evolving into a digitally enabled financial landscape, with strong advances in electronic payments, SEPA integration, open banking, and fintech collaboration. According to the Bank of Albania (2025), digital payment adoption has reduced reliance on cash and promoted formalization of economic activity, with the average number of digital payments per citizen rising from two in 2015 to 23 in 2024. Financial inclusion has improved significantly, with 78% of the population now holding at least one financial account, nearly double the level in 2015. These developments are supported by expanded payment infrastructure and innovative tools like electronic wallets, increasingly reaching underserved populations in regions with limited access to traditional banking services.

This study contributes to the extant literature in two key ways. First, by focusing exclusively on Albania as a small, emerging economy, this study is the first of its kind to provide empirical evidence on the relationship between financial innovation and economic growth in this specific context, which has been largely overlooked in prior research that predominantly focused on advanced economies or large emerging markets. Second, by examining the post-COVID-19 period, a critical phase during which financial innovation expanded markedly, this study addresses an area that remains underexplored in the literature. By filling these two gaps, the study offers insights that are directly relevant to Albania and may also inform broader policy discussions in other developing and emerging European economies, providing actionable guidance for policymakers seeking to leverage financial innovation to promote sustainable, inclusive, and long-term economic growth.

The structure of the paper is as follows. The Literature Review discusses the main theoretical and empirical contributions on financial innovation and economic growth. The Methodology section presents the data, variables, and econometric strategy, with particular emphasis on the ARDL approach. The Results section presents the empirical findings and interprets their significance. Finally, the Conclusion and Policy Implications summarize the study’s main contributions and provide recommendations for policymakers.

Literature Review

Financial Innovation and Financial Sector Development

Innovation serves as a pivotal determinant of economic advancement, functioning as a structural catalyst that not only introduces novel mechanisms and solutions but also reconfigures productive capacities, enhances allocative efficiency, and fosters systemic resilience within modern economies. According to the OECD (2004), financial innovation encompasses the full range of scientific, technological, financial, and commercial efforts involved in the creation, implementation, and advancement of new financial markets and enhanced financial instruments.

Innovation goes beyond simply creating new products, it also involves introducing new methods and approaches to address economic challenges (Kotsemir & Abroskin, 2013). In the financial sector, innovation enhances the value of financial services and instruments (McGuire & Conroy, 2013), supports improved capital formation and distribution processes (Allen, 2012; Uddinet al., 2014), contributes to the broader development of financial systems (Ozcan, 2008), and boosts the operational efficiency of financial institutions (Shaughnessy, 2015). The effectiveness of financial institutions also plays a crucial role in advancing financial development by enhancing payment systems, which in turn facilitate smoother and faster domestic and global trade transactions (Sabandi & Noviani, 2015).

The financial sector’s development is a critical driver of economic growth and can be achieved through various innovations, including the expansion of capital markets and the modernization of the banking sector. Ndako (2010) emphasizes that both an efficient capital market and a dynamic banking industry are instrumental in fostering economic expansion, primarily through improved resource allocation, enhanced capital formation, and the establishment of effective linkages between the surplus and deficit segments of the economy. Furthermore, Adusei (2013) argues that well-structured financial institutions and robust capital markets are essential not only for economic performance but also for promoting sustainable development, as they enable more productive and environmentally responsible investments. This perspective is supported by Mhadhbi (2014) and Orjiet al. (2015), who also highlight the importance of financial infrastructure in driving long-term ecological and economic progress.

However, the extent to which financial development influences growth may vary depending on the country’s specific economic conditions (Adusei, 2013). Uddinet al. (2012) further add that improvements in the financial sector can be a key engine for broader economic advancement and have a direct impact on poverty alleviation. They suggest that financial sector growth increases access to financial services, which in turn supports inclusive development. In a similar vein, Kerr and Nanda (2014) point out that expanding financial markets plays a fundamental role in enhancing the efficient use of financial resources by fostering innovation, increasing investment opportunities, and facilitating entrepreneurship.

Financial Innovation and Economic Growth

The connection between financial development and economic activity has long been recognized, both in early works (Goldsmith, 1969; Gurley & Shaw, 1967; Schumpeter & Backhaus, 2003) and more recent studies (Sumaira & Bibi, 2022; Laevenet al., 2015; Lumpkin, 2010; Qamruzzaman & Jianguo, 2017; Sekhar & Gudimetla, 2013). Recent studies suggest that financial development is a key driver of economic growth, particularly when financial innovation is included as an additional component. This expanded approach shows a statistically significant and positive relationship between financial innovation and economic growth, highlighting its crucial role in enhancing economic performance (Mollaahmetoğlu & Akçalı, 2019).

Similarly, a strong link is observed between financial innovation and economic growth. Financial innovation is widely seen as a key element of financial advancement. Historically, it has had both positive and negative impacts on economic performance (Bernier & Plouffe, 2019; Arnaboldi & Rossignoli, 2013; Becket al., 2015; Hammad Naeemet al., 2023; Olayungbo & Quadri, 2019). Supporters argue it channels savings into productive uses and boosts capital accumulation (Mishra, 2008; Naziret al., 2021). On the other hand, critics claim that complex innovations may exploit uninformed users by disguising low-value products behind difficult-to-understand structures (Allen, 2012). More recently, FinTech innovations have increased competition in the banking sector, particularly through mobile transactions, however, they have not significantly enhanced core bank performance, suggesting that traditional banking structures continue to play a crucial role (Mhlongoet al., 2025).

Other studies have reported differing effects of financial innovation on economic growth depending on the time frame, showing positive impacts in the short term and negative ones in the long term (Ansonget al., 2011; Chienet al., 2021). Conversely, Idun and Aboagye (2014) present findings revealing a negative short-term relationship between indicators such as M2/M1 and economic growth, alongside a positive association in the long term.

Although empirical data on the impact of financial innovation remains limited (Chienet al., 2021; Garcia Bassa, 2013), research suggests that financial innovation tends to exert a greater influence on economies that are more reliant on their financial systems. In cases where a bidirectional relationship exists, it is suggested that integrating financial innovation with economic growth could foster sustainable development in developing economies (Bara & Mudxingiri, 2016a).

Methodology

Data and Variables

This study employs a quantitative approach, utilizing annual time-series data for Albania from 2000 to 2023. This period reflects Albania’s post-transition two-tier banking system, encompasses major developments and ensures consistency by avoiding structural breaks characteristic of the pre-2000 era. The variables used in this study are summarized in Table I. Data were collected from the World Bank, the International Monetary Fund (IMF), and the Bank of Albania (BoA). Real GDP per capita (GDPC) is used as the dependent variable, representing economic growth, following prior empirical studies (Bara et al., 2016; Cavenaileet al., 2011; Qamruzzaman & Jianguo, 2017).

Table I. Description of the Variables

Variable category	Variable	Definitions	Expected sign
Dependent variable	GDPC	Real GDP per capita
Financial innovation	DC	Domestic credit to the private sector by financial institutions (% of GDP)	Positive
Financial innovation	MON	Money multiplier	Positive
Financial innovation	MOB	Mobile cellular subscriptions (per 100 people)	Positive
Control variable	CPI	Consumer price index	Negative
Control variable	GC	General government final consumption expenditure (% of GDP)	Positive
Control variable	CAP	Gross fixed capital formation (% of GDP)	Positive
Control variable	TRA	Trade (% of GDP)	Positive

Since financial innovation lacks a single, universally agreed-upon measure, the study employs multiple proxy variables. These proxies were selected based on prior empirical studies and specifically chosen to best capture financial innovation in the context of the Albanian economy, reflecting its financial structure and market characteristics. The first proxy is domestic credit to the private sector by financial institutions (DC), which captures the extent of access to finance for households and businesses. Greater credit availability is associated with increased productivity and growth (Guidotti & de Gregorio, 1992), and it also reflects the ongoing digital transformation in lending practices (Rzepka, 2019). The second proxy, mobile cellular subscriptions per 100 people (MOB), indicates the level of technological diffusion and the potential for accessing mobile financial services. Although not all users engage in mobile banking, higher mobile penetration supports financial inclusion and the adoption of digital financial tools (Klein & Mayer, 2011; Aker & Mbiti, 2010; Asongu, 2013; Ondiege, 2010; Bara et al., 2016). The third proxy is the money multiplier (MON), calculated as the ratio of broad money (M3) to base money (M0). It serves as a measure of financial deepening and the efficiency of the banking system in expanding liquidity and credit to the economy (Ansonget al., 2011).

To control for broader macroeconomic influences, the model includes four additional variables: Consumer Price Index (CPI) to account for inflation, General Government Final Consumption Expenditure (GC), Gross Fixed Capital Formation (CAP), and Trade (TRA), all expressed as a share of GDP. These controls help isolate the specific contribution of financial innovation to economic growth.

Stationarity Tests

To verify the stationarity of the variables and determine their order of integration, the ADF (Augmented Dickey-Fuller) test was employed. This procedure involves analyzing the n^th order differencing of each variable. The equation of this test is:

(1) $$\begin{array}{r}{x}_t=D_t{\gamma }_1+{\gamma }_2{x}_{t-1}+{\mathrm{\Sigma }}_{i=1}^n{\pi }_i\mathrm{\Delta }{x}_{t-i}+{\epsilon }_t\end{array}$$

where D_t represents deterministic components such as a constant or trend, n denotes the number of lagged differences included to eliminate autocorrelation, Δx_t–i reflects the ARMA dynamics of the error term, and ε_t is a white noise error term.

In addition to the Augmented Dickey-Fuller (ADF) test, which we have already used to check for unit roots, we also apply the Phillips-Perron (PP) test as an alternative method. While both tests aim to determine the stationarity of a time series, the PP test has become particularly popular due to its ability to handle heteroskedasticity and serial correlation in the error terms. Unlike the ADF test, which relies on a parametric autoregressive model to correct for serial correlation, the PP test uses a non-parametric approach that adjusts the test statistics directly. The equation of this test is:

(2) $$\begin{array}{r}{x}_t=D_t{\gamma }_1+{\gamma }_2{x}_{t-1}+{\epsilon }_t\end{array}$$

where ε_t is assumed to be stationary and heteroskedasticity. By correcting the standard errors of the test statistics, the PP test provides more robust results in the presence of autocorrelation and changing variance in the residuals.

ARDL Model Specification

The Autoregressive Distributed Lag (ARDL) model was employed in this study due to several key advantages over other cointegration techniques. The ARDL model is applicable regardless of sample size, making it suitable for both small and large datasets. It is also capable of handling regressors that are integrated at different levels, specifically a mix of I(0) and I(1) variables, without requiring all variables to be stationary at the same order. Additionally, the inclusion of appropriate lag structures allows the model to correct for serial correlation and address endogeneity concerns. Another strength of the ARDL framework is its ability to estimate both short-run dynamics and long-run equilibrium relationships within a single model, producing consistent and reliable results. A simplified version of the ARDL model involving variables X, Y, and Z can be specified as follows:

(3) $$\begin{array}{rl}\mathrm{\Delta }{y}_t& ={\varnothing }_1+{\gamma }_1{y}_{t-1}+{\gamma }_2{x}_{t-1}+{\gamma }_3{z}_{t-1}+{\theta }_1{\sum }_{i=1}^n\mathrm{\Delta }{y}_{t-i}\\ & +{\theta }_2{\sum }_{i=1}^n\mathrm{\Delta }{x}_{t-i}+{\theta }_3{\sum }_{i=1}^n\mathrm{\Delta }{z}_{t-i}+{\epsilon }_t\end{array}$$

where γ₁, γ₂, and γ₃ represent the long-run coefficients, collectively forming the error correction component analogous to that found in a Vector Error Correction Model (VECM). The coefficients θ₁, θ₂, and θ₃ capture the short-run dynamics.

Causality and Stability Tests

The Granger causality test is conducted to examine the directional effects between the variables, providing insights into whether financial innovations drive economic growth or if economic growth encourages further innovations in the financial sector.

In addition, stability diagnostics were performed to ensure the robustness of the estimated coefficients. Model stability was examined using the Cumulative Sum (CUSUM) and Cumulative Sum of Squares (CUSUMSQ) tests, which are widely employed to detect structural changes in time-series regression parameters.

Results

Stationarity Analysis

The stationarity test, conducted through the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) approaches, reveals that the majority of the variables are non-stationary at level but attain stationarity after first differencing, indicating integration of order one. The results are presented in Table II. lnMOB stands out as the only variable stationary in level form, suggesting it is integrated of order zero. lnDC, however, did not satisfy stationarity conditions at the level, requiring its transformation into first differences (ΔlnDC) for use in the empirical model. As no variable is integrated beyond order one, the dataset satisfies the prerequisites for co-integration testing.

Table II. Unit Root Test

Variables	Augmented Dickey-Fuller test statistic		Phillips-Perron test statistic		Integration
	At level	First difference	At level	First difference
lnGDPC	−1.990547	−3.110033	−1.990547	−3.104180	I(1)
	(−2.998064)	(−3.004861)**	(−2.998064)	(−3.004861)**
ΔlnDC	−1.651794	−4.671585	−1.740190	−4.650365	I(1)
	(−3.004861)	(−3.788030)*	(−3.004861)	(−3.788030)*
lnMON	−1.286835	−3.580625	−1.400738	−3.555616	I(1)
	(−3.020686)	(−3.029970)**	(−3.020686)	(−3.029970)**
lnMOB	−13.29469		−10.55811		I(0)
	(−3.769597)*		(−3.769597)*
lnCPI	−0.497839	−4.298286	−0.497839	−4.298286	I(1)
	(−2.998064)	(−3.769597)*	(−2.998064)	(−3.769597)*
lnGC	−2.199278	−3.870095	−2.266237	−3.997843	I(1)
	(−2.998064)	(−3.769597)*	(−2.998064)	(−3.769597)*
lnCAP	−0.467925	−5.482931	−0.517846	−5.389557	I(1)
	(−2.998064)	(−3.769597)*	(−2.998064)	(−3.769597)*
lnTRA	−2.959145	−5.547850	−2.813529	−7.289937	I(1)
	(−2.998064)	(−3.769597)*	(−2.998064)	(−3.769597)*

Long-Run and Short-Run Analysis

To assess the existence of a long-run relationship among the variables, the ARDL bounds testing approach was employed. The hypotheses are formally stated as follows:

H₀: There is no long-run (levels) relationship among the variables.

H₁: There is a long-run (levels) relationship among the variables.

The computed F-statistic substantially exceeds the 1% upper critical bound, given this result, the null hypothesis is rejected. The presence of a long-term relationship between economic growth and the selected financial innovation proxies is confirmed, as presented in Table III.

Table III. ARDL Bound Testing Results

Model	Test value	F-value	Co-integration
		33.23782	Present
k	10%	5%	1%
I(0)	2.277	2.730	3.864
I(1)	3.498	4.163	5.694

The presence of a long-run co-integration relationship was validated using the bounds testing approach, with economic growth specified as the dependent variable. Following this confirmation, we proceeded to estimate the long-run elasticities by employing the long-run form of the ARDL models. The corresponding long-run coefficient estimates are presented in Table IV.

Table IV. Long-Run Dynamics Estimation

Variables
C	ΔlnDC	lnMON	lnMOB(-1)
−23.14005	−0.510215	0.713962	0.300868
(−8.083313)*	(−3.530077)*	(2.221389)**	(2.014481)***
lnCPI(-1)	lnGC	lnCAP(-1)	lnTRA
2.472881	2.349754	2.548839	0.845002
(4.242187)*	(4.951488)*	(7.885551)*	(2.476716)**

The variable DC has a negative and statistically significant coefficient, indicating that a 1% increase in domestic credit leads to a 0.51% decline in economic growth. This result indicates that domestic credit may be excessive, surpassing an optimal threshold, or predominantly directed toward consumption rather than productive investment. In the Albanian context, these factors may also reduce the effectiveness of financial innovations.

The variable MON shows a positive and statistically significant effect on growth. A 1% rise in the money supply results in a 0.71% increase in economic output, reinforcing the idea that greater liquidity stimulates investment and overall economic activity.

The variable MOB holds a positive coefficient, but it is not statistically significant at the 5% level. While MOB is expected to enhance growth, the model does not provide sufficient evidence to confirm its long-term impact in this context.

As for the remaining variables, CPI, GC, CAP and TRA, each exerts a positive and statistically significant influence, underlining their essential role in sustaining long-term economic growth.

The observed relationship between economic growth and the variables DC and MON confirms that financial development remains a vital driver of long-run growth performance in the Albanian economy.

In the short-run dynamics presented in Table V, DC continues to have a negative and statistically significant effect on economic growth, with a coefficient, indicating that short-term credit allocation may not be efficiently directed toward productive sectors. MON, although showing a positive coefficient, is not statistically significant, suggesting that its short-term influence on economic activity is limited or uncertain within the model's confidence interval. The variable MOB displays a nuanced impact, its current value negatively and significantly affects growth, while its lagged value becomes positive and highly significant, indicating that the positive effects of financial mobility may emerge over time rather than immediately.

Table V. Short-Run Dynamics Estimation

	Variables
C	−15.09128	(−6.469638)*
lnGDPC(-1)	0.347828	(3.625983)*
ΔlnDC	−0.332748	(−3.266897)**
lnMON	0.465625	(2.235675)***
lnMOB	−0.198472	(−2.650696)**
LnMOB(-1)	0.394690	(4.623572)*
lnCPI	0.507031	(0.727994)
lnCPI(-1)	1.105711	(1.399108)
lnGC	1.532443	(4.738105)*
lnCAP	1.122798	(5.675440)*
lnCAP(-1)	0.539483	(2.337903)**
lnTRA	0.551086	(3.083742)**
R2	0.996929
Adj. R2	0.992707
F-statistic & (prob. value)	236.1061	(0.000000)
Durbin-Watson statistic	2.533076

Causality Analysis

To explore the directional linkages between financial innovation and economic growth, Granger causality tests were conducted, with the results summarized in Table VI. The findings reveal an asymmetric relationship: the money multiplier significantly Granger-causes economic growth, suggesting that improvements in monetary efficiency contribute to Albania’s GDP expansion. On the other hand, indicators such as mobile subscriptions and capital formation appear to be shaped by the trajectory of economic growth, implying that innovation often responds to, rather than initiates, growth. Notably, domestic credit to the private sector shows no significant causal relationship in either direction, pointing to a limited role in predicting or driving economic performance. These results underline the multifaceted nature of financial innovation’s impact on economic growth.

Table VI. Ganger Causality Test

Causality direction	F-Statistic	p-Value	Causality direction	F-Statistic	p-Value
ΔlnDC ➜ lnGDPC	0.12637	0.7261	lnGDPC ➜ ΔlnDC	1.73607	0.2033
lnMON ➜ lnGDPC	10.4986	0.0048	lnGDPC ➜ lnMON	4.04651	0.0604
lnMOB ➜ lnGDPC	1.24980	0.2775	lnGDPC ➜ lnMOB	12.5605	0.0022
lnCPI ➜ lnGDPC	1.55281	0.2271	lnGDPC ➜ lnCPI	0.13166	0.7205
lnGC ➜ lnGDPC	2.70363	0.1157	lnGDPC ➜ lnGC	0.01816	0.8941
lnCAP ➜ lnGDPC	0.52864	0.4756	lnGDPC ➜ lnCAP	14.7389	0.0010
lnTRA ➜ lnGDPC	0.07060	0.7932	lnGDPC ➜ lnTRA	2.57682	0.1241
ΔlnDC ➜ lnCPI	0.30584	0.5867	lnCPI ➜ ΔlnDC	1.80265	0.1952
lnMON ➜ lnCPI	3.15524	0.0936	lnCPI ➜ lnMON	5.62849	0.0297
lnMOB ➜ lnCPI	0.40516	0.5320	lnCPI➜ lnMOB	7.03097	0.0157
lnGC ➜ lnCPI	0.39191	0.5384	lnCPI ➜ lnGC	0.89284	0.3560
lnCAP ➜ lnCPI	0.97724	0.3347	lnCPI ➜ lnCAP	10.7756	0.0037
lnTRA ➜ lnGDPC	0.25871	0.6166	lnCPI ➜ lnTRA	1.14390	0.2976
lnMON ➜ ΔlnDC	1.98940	0.1764	ΔlnDC ➜ lnMON	1.54103	0.2313
lnGC ➜ ΔlnDC	0.00426	0.9486	ΔlnDC ➜ lnGC	3.04826	0.0970
lnCAP ➜ ΔlnDC	3.41385	0.0803	ΔlnDC ➜ lnCAP	2.73033	0.1149
lnTRA ➜ ΔlnDC	0.34114	0.5660	ΔlnDC ➜ lnTRA	0.03335	0.8570
lnGC ➜ lnMON	3.99244	0.0620	lnMON ➜ lnGC	0.55239	0.4675
lnCAP ➜ lnMON	4.84381	0.0419	lnMON ➜ lnCAP	1.46363	0.2429
lnTRA ➜ lnMON	0.14816	0.7051	lnMON ➜ lnTRA	0.11155	0.7425
ΔlnDC ➜ lnMOB	0.05553	0.8164	lnMOB ➜ ΔlnDC	0.61746	0.4422
lnMON ➜ lnMOB	1.92489	0.1843	lnMOB ➜ lnMON	0.36317	0.5552
lnGC ➜ lnMOB	0.29537	0.5931	lnMOB ➜ lnGC	0.99910	0.3301
lnCAP ➜ lnMOB	10.0888	0.0050	lnMOB ➜ lnCAP	13.8901	0.0014
lnTRA ➜ lnMOB	0.68164	0.4193	lnMOB ➜ lnTRA	1.90720	0.1833
lnTRA ➜ lnGC	0.00045	0.9833	lnGC ➜ lnTRA	0.28958	0.5964
lnGC ➜ lnCAP	0.05166	0.8225	lnCAP ➜ lnGC	3.61408	0.0718
lnTRA ➜ lnCAP	18.1144	0.0004	lnCAP ➜ lnTRA	0.00631	0.9375

Model Stability Analysis

The stability of the estimated ARDL model was assessed using the Cumulative Sum (CUSUM) and Cumulative Sum of Squares (CUSUMSQ) tests. As illustrated in Fig. 1, the plots of both the cumulative sum of recursive residuals and their squared values remain within the 5% significance boundaries. This visual and statistical evidence confirms that the estimated model exhibits parameter stability over the sample period, supporting the robustness of the relationship between financial innovation and economic growth.

Fig. 1. Stability test. Source: Author’s calculations.

Conclusion and Policy Implications

This study offers novel empirical evidence on the nexus between financial innovation and economic growth in Albania as a small, emerging European economy. Using the Autoregressive Distributed Lag (ARDL) framework for the period 2000–2023, the analysis provides robust evidence of a stable long-run relationship between innovation in the financial sector and overall economic performance. The money multiplier and domestic credit to the private sector emerge as critical long-term drivers of growth in Albania, while mobile cellular subscriptions and private credit exert significant short-run effects, highlighting the role of financial inclusion and access to innovative services in stimulating immediate economic activity.

In the Albanian context, monetary efficiency improvements, captured by the money multiplier, were found to Granger-cause GDP growth, whereas mobile cellular subscriptions and capital formation were mainly influenced by economic expansion. These findings underline the dual role of financial innovation, acting both as a catalyst for growth and as a reflection of evolving macroeconomic conditions. The stability and robustness of the estimated model reinforce the reliability of these conclusions.

The insights derived from Albania provide lessons that, while specific to the country, may offer guidance for similar small emerging European economies. The experience demonstrates that targeted financial innovations, such as digital payments, fintech development, and expanded access to modern credit instruments, can accelerate growth, enhance financial inclusion, and reduce reliance on cash-based transactions. Policymakers in Albania can draw on this evidence to design adaptive frameworks that encourage innovation while safeguarding financial stability. However, careful attention is needed, as the effects of domestic credit can, in some cases, reduce growth. Forward-looking regulations, reduced bureaucratic frictions, and incentives for the responsible adoption of financial technologies are essential for realizing these benefits.

Future research should build on this study by conducting comparative analyses including Albania and other developing economies. Such efforts would enhance our understanding of how financial innovation can act as a strategic driver of sustainable, inclusive, and long-term economic transformation in emerging Europe.

Conflict of Interest

Authors declare there are no conflict of interest or competing interests to declare.