Decoding Digital Synergies: How Mechatronic Systems and Artificial Intelligence Shape Banking Performance Through Quantile-Driven Method of Moments

Abstract

This study investigates the heterogeneous impact of bank automation on institutional performance, emphasizing the role of mechatronic systems like automated teller machines (ATMs) and artificial intelligence-based tools such as chatbots and robo-advisors. Using Method of Moments Quantile Regression (MMQR), the analysis examines how these technologies influence key performance indicators, including return on equity (ROE), in the European Union (EU) banking sector from 2017 to 2022. The MMQR method allows for the differentiation of the effects of automation technologies by distinguishing between hardware-based mechatronic systems and software-driven AI solutions, providing a nuanced perspective on the digital transformation within the banking sector. The results highlight the heterogeneous effects of economic, financial, and institutional factors on banking performance in the EU. They emphasize the need for differentiated policy interventions to reduce performance gaps between EU economies and ensure that banks across all member states can leverage financial and technological advancements to enhance profitability. The findings underline the importance of strategic interventions to address digitalization disparities, promote financial inclusion, and establish a regulatory framework that fosters transparency, cybersecurity, and equitable access to AI-driven financial services.

1. Introduction

The adoption of robotic automation in the banking sector represents one of the most significant digital transformations in the financial industry, having a profound impact on the way banks structure their operations and deliver their services [1,2]. The deployment of robots and automated technologies enables financial institutions to improve operational efficiency by reducing human error and speeding up transaction, document processing, and data management processes [3,4]. At the same time, automation optimizes costs by reducing the need for human resources for repetitive and voluminous tasks, which contributes to significant savings in the long run [5]. Another major benefit of this technology is the increase in the quality of customer service by improving response times and providing personalized, fast, and efficient solutions [6,7]. Thus, robot adoption in banking not only supports banks in meeting regulatory and security requirements but also enables them to remain competitive in an increasingly digitized and dynamic financial landscape [8,9,10].

Banking automation (Figure 1) includes both the hardware component, represented by mechatronic systems, such as automated teller machines (ATMs) and robots, and the digital component, which is based on artificial intelligence (AI) and robotic process automation (RPA) solutions [11,12,13,14]. Mechatronic systems combine mechanical, electronic, and control components with numerical computing systems, enabling intelligent motion control, ensuring the efficient and flexible operation of devices used in the banking sector [15,16].

On the one hand, hardware automation involves the use of mechatronic systems to optimize banking processes, reducing the need for direct interaction between customers and employees [17]. Automated teller machines (ATMs) have evolved significantly from simple cash withdrawal devices to complex systems that allow deposits, interbank transfers, currency exchange, and even video-assisted interactions [18,19,20]. In addition, the use of physical robots in bank branches to guide customers or provide information about financial products is becoming an increasingly common practice and has already been implemented in Japan and some European countries [21,22].

These innovations contribute to better management of the bank’s resources by automating repetitive processes and reducing the need for direct interaction between staff and customers, leading to significant savings in time and resources [23].

Moreover, the implementation of these advanced technologies optimizes transaction processing times and reduces the risk of human error, leading to greater accuracy in handling customer requirements [24,25]. For example, ATMs that allow withdrawals as well as deposits and interbank transfers make these services available at any time, without the need for employee intervention, thus improving the bank’s operational efficiency. Reducing the need for staff for routine administrative tasks also allows human resources to be allocated to activities that add more value, such as personalized financial advice. These technological advances not only optimize costs and internal processes, but also enhance the customer experience by providing fast, accessible, and high-quality services [26].

On the other hand, software automation (digital components) uses AI-driven solutions and RPA to optimize internal processes and customer interactions [27,28]. Banking chatbots, built on Natural Language Processing (NLP) and machine learning technologies, are increasingly being used to handle customer queries, reducing response times and streamlining banking relationships [29,30,31]. Robo-advisors are also increasingly replacing financial analysts in the process of providing investment recommendations, using predictive models based on the analysis of users’ historical and behavioral data [32,33]. These software solutions, which include both artificial intelligence automation and virtual robots, help to manage customer interactions more efficiently, optimizing the time and resources allocated to respond to requests and deliver personalized solutions [34,35].

Moreover, software automation helps streamline banks’ internal processes, from data management and transaction analysis to the provision of financial advisory services, significantly improving their efficiency and accuracy [36]. In this context, the use of robo-advisors is gradually replacing traditional methods of financial advice [37], having a significant impact on the way clients’ investments and portfolios are managed. Also, the integration of these technologies into an already automated banking ecosystem contributes to a more seamless and personalized experience for customers, as they benefit from fast and accurate assistance, whether they are in front of an ATM or interacting with a chatbot [38].

Another aspect of software automation is process automation through RPA, a technology used for compliance operations management, financial reporting, risk analysis, and fraud detection [39,40]. According to a study from Grand View Research, the banking, financial services, and insurance (BFSI) sector had a 29% share of the global RPA market, as the banking industry relies heavily on repetitive tasks [41]. By implementing RPA, banks are able to eliminate cyclical activities, such as document verification or transaction validation, which reduces processing time and decreases the risk of human error [17,42,43].

The impact of robotization in the banking sector is significant, both in terms of operational efficiency and workforce restructuring [44,45]. Automating core processes enables banking institutions to reduce operational costs, minimize human error, and speed up workflows [46,47]. Implementing AI and RPA in financial systems has demonstrated up to 30% reduction in operational costs and 25–50% in efficiency gains, according to recent estimates [42].

In addition to the economic benefits, automation also has implications for the banking workforce [48,49,50]. The digitalization process is leading to a reduction in the number of traditional jobs, especially in administrative and operational roles. However, this transition creates new opportunities for employees, who can be reoriented towards high value-added activities such as data analytics and personalized financial advice [25,51,52].

At the same time, banking automation entails significant challenges related to cybersecurity [53], IT infrastructure integration, and consumer acceptance of new technologies [54]. Increasing reliance on AI and RPA systems creates information security and user data protection risks, and advanced protection mechanisms against cyber-attacks are needed [17]. According to a study [55], in 2023, 3348 cyber-attacks were reported in the financial sector, a significant increase from 1829 in 2022.

The aim of this paper is to analyze the interaction between automation technologies and banking performance, focusing on the role of mechatronic systems, such as automated teller machines (ATMs), and artificial intelligence-based tools, such as chatbots and robo-advisors. Using the MMQR, the research examines how these technologies influence key indicators of banking performance, taking into account the heterogeneity of the EU banking sector between 2017 and 2022. The novel element of the study is the simultaneous integration of mechatronic automation, as measured by the number of ATMs, and artificial intelligence-based solutions, using DESI indicators and R&D investment, to analyze the impact of digitalization on banking performance in the context of the diversity of the European banking sector.

Analyzing the impact of robotic automation and artificial intelligence-based solutions on bank performance is a topic of growing interest in the literature, but existing studies have not systematically explored how these technologies influence the profitability of financial institutions in relation to their performance level. While much of the research has focused on the general effects of automation on the banking sector, it has often neglected variations in these effects across different institutions’ performance. In particular, the influence of automation on profitability may differ significantly between high performing banks and those with lower operational efficiency or financial performance, an issue that has not yet been sufficiently addressed in the literature. The main contribution of this study lies in applying the MMQR approach to capture the heterogeneity of the impact of automation and digitalization on return on equity (ROE) in the EU banking sector. MMQR is the best approach to capture the heterogeneity of the banking sector because it allows for modeling economic relationships at different levels of distribution, thus capturing specific effects across distribution quantiles, not just the average [51]. In the banking sector, large and small banks may react differently to economic changes, and MMQR can differentiate their impact, providing a more detailed analysis of the diversity in financial performance and associated risks [50]. This method is also useful for analyzing bank behavior in times of economic or regulatory crisis, where banks in different segments may experience significantly different effects on financial stability [45].

The novelty of the study lies in the simultaneous integration of physical automation, measured by the number of ATMs, and AI-driven solutions, reflected by the DESI–Integration of Digital Technology (IDT), DESI–Human Capital (HC) and the proportion of internet banking users, together with R&D investments. Unlike previous approaches, this analysis allows not only an estimation of the technological effects on banking performance, but also a deeper understanding of the mechanisms through which digitalization shapes the financial sector according to the level of competitiveness of each institution.

In terms of implications for policymakers and regulators, the results provide relevant insights into the need to adapt regulatory and technology investment strategies to the heterogeneous realities of the banking sector. The identification of the asymmetric effects of automation and AI on the profitability of financial institutions suggests that standardized policies are not sufficient to ensure a smooth digital transition across EU member states. Thus, the implementation of customized measures to support the adoption of new technologies according to the specificities of each national banking system becomes imperative. In addition, stimulating investment in research and development in the field of financial technologies is essential to increase competitiveness and reduce the digital divide between member states.

As regards the research gap in the literature, most of the available studies have focused either on the general impact of digitalization on the financial industry or on the effects of automation on employment, without investigating in depth how these transformations influence the distribution of banking performance. Also, existing empirical approaches have not used advanced regression methodologies to analyze the heterogeneity of technological effects on different segments of the financial market.

This study advances existing research by applying the MMQR method to investigate the heterogeneous effects of automation and digitalization on bank performance, simultaneously integrating mechatronic technologies and artificial intelligence-based solutions and providing a detailed analysis of the diversity of their impact on the profitability of financial institutions.

The paper is divided into six sections: Section 2 reviews the relevant literature; Section 3 explains the methodology and data; Section 4 focuses on interpreting the results; Section 5 discusses the results; and Section 6 concludes with the study’s conclusions, practitioners’ implications, future research, and limitations.

2. Literature Review

The study explores three research directions: the adoption of automation and artificial intelligence in banking, the impact of automation through mechatronic systems and AI on banking efficiency and performance, and the transformative effects of automation on the labor market, particularly concerning banking employees.

2.1. Adopting Automation and Artificial Intelligence in Banking

In recent decades, automation has significantly enhanced banking services, with a positive impact on many users [56,57]. Although automation in banking brings multiple benefits to consumers, empirical studies show that consumers still prefer human interactions and perceive AI performance as inferior to that of human employees [58]. As a result, consumer acceptance of automation is generally low [58,59,60,61].

Over time, various theories have been developed to identify factors that influence technology adoption. A significant example is the Unified Theory of Acceptance and Use of Technology (UTAUT), which is considered the most comprehensive model for predicting consumer behavior, including in the banking sector [62,63], with the ability to explain about 70% of the variation in behavioral intentions. This theory places particular emphasis on the role of performance expectation as the main determinant of technology adoption [64]. Performance expectancy refers to the degree to which individuals believe that automation can help improve the performance of jobs or other activities [65,66]. It is considered the strongest predictor of behavioral intentions, which in turn significantly influence technology use [65,66].

In the banking and finance sector, performance expectancy plays an important role in the intention to use and use of automation [67,68]. Following a meta-analysis of 57 studies and 58 datasets, Baptista and Oliveira [69] identified a positive and significant relationship between performance expectancy and the intention to use mobile banking. This intention is closely related to the actual use of these services. Also, Akhlaq and Ahmed [70] and Wang et al. [67] reported that performance expectancy significantly influences E-banking acceptance. During the pandemic, Gan et al. [71] observed that performance expectation plays a determining role in consumers’ intention to adopt online financial robo-advisors. The study on a large sample of consumers in China by Cheng-Xi Aw et al. [64] emphasizes the importance of performance expectation in the acceptance of robo-advisory services, while Rühr et al. [72] demonstrated that this factor has a significant positive impact on the intention to use an investment management system. In their study of young investors in Malaysia and Sweden, Nourallah [73] emphasized that trust in financial robo-advisors and intentions to use this technology are closely related to performance expectation.

Thus, the following research hypothesis can be postulated:

Hypothesis 1 (H1).

Higher profitability in banking institutions, as measured by return on equity, enhances more efficient utilization of robotic automation.

High profitability provides banking institutions with the financial capacity to invest in advanced technologies such as mechatronic systems (e.g., smart automated teller machines) and artificial intelligence-based solutions (e.g., chatbots, robot advisors, predictive analytics). These technologies entail significant upfront costs for acquisition, deployment, and integration into existing banking infrastructure, as well as the need for resources for ongoing maintenance and adaptation.

Therefore, banks with a higher return on equity (ROE) are better positioned to sustain these investments, leading to more effective adoption of automation technologies and increased operational efficiency. In this context, ROE is not only an outcome of digitalization, but also an enabler of the transition to a banking model based on automation and artificial intelligence.

2.2. The Impact of Automation Through Mechatronic Systems and AI on Banking Efficiency and Performance

As automation and artificial intelligence become increasingly embedded in the banking sector, mechatronic systems and AI solutions are gradually starting to replace human employees in various financial operations. However, the role of human employees remains essential, especially in direct interactions with customers, where the human factor significantly influences consumer experience and loyalty to the banking institution.

According to studies, in the retail financial industry, consumers have high bargaining power, as they are able to compare products and services offered by different financial intermediaries [74]. In such a context, employee performance becomes a determining factor [75,76], and employees play an important role in meeting consumer needs [77,78]. Research on the relationship between customers and banks emphasizes that direct interactions between employees and customers are essential for maintaining long-term relationships, which contributes to the profitability of financial institutions [79,80]. Moreover, consumer perceptions of service quality are largely influenced by employee behavior, and employees are considered to be mainly responsible for a positive or negative experience with the bank [81].

However, technological advancement is driving a significant transformation in the industry, with artificial intelligence becoming central to redefining banking processes. While, on the one hand, human interaction remains essential in maintaining trusting relationships with customers, on the other hand, AI is consolidating its position in optimizing operational efficiency, risk analysis, and service personalization.

In recent years, research on the impact of artificial intelligence in the banking sector has highlighted both the benefits and challenges of this technology. Bibliometric studies conducted by Milana and Ashta [82], Manta et al. [11], and Fares et al. [83] highlight the role of AI in optimizing banking efficiency, managing risk, and driving innovation, but also the need for further investigation to fully understand its effects on the financial industry.

Analyzing recent trends, Doumpos et al. [84] highlighted the applicability of AI in risk assessment, operational efficiency, and banking performance, while Jena et al. [85] identified the increasing influence of AI and IoT in financial engineering. Other studies [86], such as the one conducted on a university sample, showed that demographic factors such as age and education level influence trust in banks, while Haddad [87] highlighted the need to improve the use of AI in bank accounting in Jordan.

In terms of AI adoption, Polireddi [88] identified determinants such as perceived usefulness, perceived risk, and social norms, while Gyau et al. [89] showed that AI contributes to improving the return on banking assets, although in the long run it may create performance challenges [90,91,92,93].

Thus, the following research hypothesis can be formulated:

Hypothesis 2 (H2).

The heterogeneous impact of AI solutions on banking performance has a differentiated effect across banks with varying profitability levels.

The adoption and effectiveness of artificial intelligence (AI)-based solutions in the banking sector do not have a uniform effect on institutional performance. Banks with high profitability (as measured by ROE) can more easily access advanced technologies and sustain the costs of staff training, systems integration, and data analytics, thereby realizing greater benefits from the use of AI. In contrast, institutions with low profitability may have difficulties in fully implementing these solutions or may use less sophisticated technologies, leading to more modest effects on performance.

This hypothesis reflects a structural differentiation in how banks leverage AI to improve their efficiency, service, and competitiveness, and the quantized moments regression (MMQR) allows us to capture this heterogeneity across different performance levels.

2.3. Labor Market Transformation and Challenges for Employees and Institutions

Banking automation, supported by technologies such as artificial intelligence (AI), has rapidly transformed the banking sector [94,95,96,97,98], improving efficiency and customer satisfaction, but also bringing significant challenges for employees and organizations. In this context, recent studies highlight the impact of automation on banking processes and the role of AI in optimizing them.

A study by Akilimalissiga [99] examines the impact of banking automation in South Africa, highlighting how it boosts productivity and efficiency, but also how it generates negative effects such as job losses and changes in skill requirements. Mohammad [100] instead investigates UAE employees’ perceptions of automation and its impact on customer satisfaction, showing that perceptions of job security are key to maintaining a balance between technological efficiency and human resource stability.

In another study, Rajput [101] emphasizes the importance of implementing intelligent process automation (IPA) for improving operational efficiency, reducing errors, and increasing customer satisfaction, as well as addressing strategic and organizational readiness challenges. Also, Adewumi [102] and Lakshmi [103] highlight how artificial intelligence and RPA have helped to increase efficiency and improve customer experience, but emphasize the need to address cybersecurity, regulatory, and employee retraining challenges.

In South Asia, the implementation of RPA has led to the creation of new jobs in technology and analytics, while traditional jobs have shrunk significantly [104]. These changes are underpinned by business intelligence data, which enable banks to optimize product and service portfolios, better meeting customer needs [105]. In this regard, RPA plays a key role in reducing human error and improving the efficiency of operational processes [106].

Thus, the following research hypothesis can be postulated:

Hypothesis 3 (H3).

The interplay between R&D spending and the initial level of digitalization is significant for evaluating the effectiveness of innovative investments.

A bank’s initial level of digitalization determines its ability to leverage subsequent investments in research and development (R&D). Institutions that already have a robust digital infrastructure (including automated systems, AI interfaces, and integrated digital platforms) can more effectively integrate the results of innovative projects, thus maximizing the return on innovative investments. On the other hand, in banks with a low level of digitalization, the impact of R&D on performance is more limited, as the lack of an adequate technological base reduces the ability to implement innovative solutions.

This hypothesis highlights that the positive effect of R&D on bank performance is conditional on the degree of pre-existing digitalization, and quantile analysis allows for capturing these interactions at different levels of institutional performance.

In order to establish the connection between the gaps identified in the literature and the hypotheses formulated in this study, we need to highlight the under-explored aspects and how they lead to the need to test these hypotheses.

In the literature, most studies on automation in the banking sector focus either on the general impact of robots and AI on operational efficiency or on specific technological aspects, without differentiating the effects according to the financial structure of banking institutions. A significant gap is the absence of analyses examining how different levels of profitability and digitalization influence the benefits of automation and AI. This limitation justifies the formulation of our hypotheses.

Thus, our hypotheses are grounded on the need to clarify issues insufficiently addressed in the literature, contributing to a more detailed understanding of the relationship between automation, artificial intelligence, and banking performance.

3. Materials and Methods

The study investigates how banking performance is influenced by automation through mechatronic systems and AI in the EU countries. The selected time is six years (2017–2022), which was chosen to capture significant trends in the evolution of automation in the banking sector, as well as its impact on banking performance, considering the major economic and technological transformations that took place during this time. Additionally, the 6-year period ensures a relevant analysis of economic and financial data, allowing for the identification of meaningful relationships between the studied variables. To reach the goal of our study, we selected a group of variables, as presented in Table 1. Data on financial indicators were collected from the International Monetary Fund, “Financial Soundness Indicators” [106] and from the Eurostat database, European Commission, and European Central Bank [107,108,109].

The choice of these variables is justified by the multidimensional exploration of the effect that banking performance has on robotic process automation and digitalization in EU countries.

Automation has become a fundamental element in the transformation of the banking sector, significantly impacting operational efficiency and customer interaction. In this context, ATMs are relevant as mechatronic systems, while chatbots and robo-advisors represent advanced software technologies, both contributing to the digitalization process.

ATMs (automated teller machines) are considered mechatronic systems due to their integration of mechanical engineering, electronics, and software, characteristics that define the concept of mechatronics. According to Bolton [25], mechatronics involves the combination of mechanics, electronics, and computer science to develop efficient automated solutions. ATMs perform tasks such as cash dispensing, deposit processing, and account balance verification through advanced electromechanical components. Furthermore, these devices are integrated with complex software systems that enable connectivity to central banking infrastructures, ensuring real-time transaction processing [110].

Simultaneously, software-based automation, illustrated by the use of banking chatbots and robo-advisors, has fundamentally changed how banks interact with their clients. These technologies leverage artificial intelligence (AI) and natural language processing (NLP) algorithms to understand and respond to user requests. For instance, banking chatbots can autonomously handle simple interactions, such as account balance inquiries or payment initiation, while robo-advisors provide personalized financial recommendations by analyzing clients’ financial data [111]. These software solutions reduce dependence on human personnel while enhancing the accessibility of financial services [112].

Integrating ATMs as mechatronic robots and chatbots or robo-advisors as software solutions demonstrates the convergence of mechanical and digital technologies in the banking sector. While ATMs optimize physical operations through advanced hardware mechanisms, chatbots and robo-advisors extend automation into the digital realm, facilitating a more personalized user experience [113]. As emphasized by Hashstudioz Technologies [114], the application of artificial intelligence in fintech is reshaping the global financial ecosystem by enhancing its performance and efficiency. This combined approach allows banks to better meet the demands of the modern market.

Therefore, ATMs, chatbots, and robo-advisors are significant components of banking automation, contributing to increased efficiency and enabling banks to adapt to the demands of an increasingly digitalized market. By leveraging these solutions, the banking sector not only improves its operational performance but also positions itself as a leader in digital transformation within the global economy.

Furthermore, in the absence of specific data on banks’ investments in artificial intelligence at the level of each country in the European Union, the use of proxy indicators is an appropriate solution to approximate the adoption of AI-based solutions in the banking sector. This approach allows an indirect assessment of the phenomenon by correlating it with indicators that reflect both the integration of digital technologies in the banking environment and their accessibility and use by the population.

A first line of analysis focuses on the degree of digitalization in the banking sector, a fundamental aspect for the implementation of AI-based solutions. In this context, a relevant indicator is the Digital Economy and Society Index–Integration of Digital Technology (DESI–IDT), which measures the adoption of digital technologies by businesses, including the use of artificial intelligence, Big Data, and automation solutions.

Complementing this institutional perspective, a second dimension of analysis focuses on digital accessibility and inclusion, which are key determinants in the uptake of AI-enabled financial services. A key indicator in this respect is the Digital Inclusion Index (DESI–Human Capital sub-index), which reflects the level of digital literacy of the population and, implicitly, the readiness of users to use automated digital banking services. Similarly, the level of internet penetration and the use of mobile devices to access financial services provide further indications of the accessibility of digital infrastructure, a key factor for the development of AI-based banking.

A third analytical dimension is user behavior and adoption of digital banking services, which are key to assessing the impact of AI-based technologies on bank–customer interactions. In this respect, a relevant indicator is the percentage of the population using internet banking services, with Eurostat data reflecting general trends in the migration towards automated financial services. While a direct indicator on the number of interactions between customers and bank chatbots would provide a more accurate measure of AI adoption in the banking sector, such data are not systematically reported at the national level.

Table 1. Variable description and data sources.

Acronym	Definition	Variable Type	Unit of Measure	Time Period	Data Source
ROE	Return on Equity	Dependent variable	Percentage	2017–2022	Financial Soundness Indicators from International Monetary Fund
ATM	Number of Automated Teller Machines (per 100,000 adults)	Independent variable	The total number of ATMs for every 100,000 adults in a country	2017–2022	Eurostat
EBANK	Individuals using the internet for internet banking: E-banking	Independent variable	Percentage	2017–2022	Eurostat
DESI_IDT	Digital Economy and Society Index (DESI)—Integration of Digital Technology	Independent variable	Score	2017–2022	European Commission
DESI_HC	Digital Economy and Society Index (DESI)—Human Capital	Independent variable	Score	2017–2022	European Commission
DIGINCL	Digital inclusion—Frequency of internet access: once a week (including every day)	Independent variable	Percentage	2017–2022	Eurostat
RD	Research and Development expenditure	Control variable	Percentage of GDP (%)	2017–2022	Eurostat
GDP	Real GDP growth rate	Control variable	Annual percentage change (%)	2017–2022	Eurostat
BANKEMPL	Number of employees in the banking sector	Control variable	Total number of employees	2017–2022	European Central Bank
MOMENT 1	Moment condition 1 derived from the quantile-specific transformation of the residuals under MMQR. Used as an internal instrument to address heterogeneity and endogeneity	Instrumental variable	Quantile-transformed residual component	2017–2022	Computed internally based on Machado & Silva [115] methodology
MOMENT 2	Moment condition 2 reflecting the second-order transformation in the MMQR. Captures distributional asymmetries and complements MOMENT 1 in modeling conditional heteroskedasticity	Instrumental variable	Quantile-adjusted expectation operator	2017–2022	Computed internally based on Machado & Silva [115] methodology

Source: Author’s own calculation.

Table 1. Variable description and data sources.

Acronym	Definition	Variable Type	Unit of Measure	Time Period	Data Source
ROE	Return on Equity	Dependent variable	Percentage	2017–2022	Financial Soundness Indicators from International Monetary Fund
ATM	Number of Automated Teller Machines (per 100,000 adults)	Independent variable	The total number of ATMs for every 100,000 adults in a country	2017–2022	Eurostat
EBANK	Individuals using the internet for internet banking: E-banking	Independent variable	Percentage	2017–2022	Eurostat
DESI_IDT	Digital Economy and Society Index (DESI)—Integration of Digital Technology	Independent variable	Score	2017–2022	European Commission
DESI_HC	Digital Economy and Society Index (DESI)—Human Capital	Independent variable	Score	2017–2022	European Commission
DIGINCL	Digital inclusion—Frequency of internet access: once a week (including every day)	Independent variable	Percentage	2017–2022	Eurostat
RD	Research and Development expenditure	Control variable	Percentage of GDP (%)	2017–2022	Eurostat
GDP	Real GDP growth rate	Control variable	Annual percentage change (%)	2017–2022	Eurostat
BANKEMPL	Number of employees in the banking sector	Control variable	Total number of employees	2017–2022	European Central Bank
MOMENT 1	Moment condition 1 derived from the quantile-specific transformation of the residuals under MMQR. Used as an internal instrument to address heterogeneity and endogeneity	Instrumental variable	Quantile-transformed residual component	2017–2022	Computed internally based on Machado & Silva [115] methodology
MOMENT 2	Moment condition 2 reflecting the second-order transformation in the MMQR. Captures distributional asymmetries and complements MOMENT 1 in modeling conditional heteroskedasticity	Instrumental variable	Quantile-adjusted expectation operator	2017–2022	Computed internally based on Machado & Silva [115] methodology

Source: Author’s own calculation.

Thus, the combined use of indicators regarding banking digitalization, digital inclusion, and the use of digital banking services provides a robust methodological approach for indirectly assessing the adoption of AI-based solutions in banking. This method enables a balanced analysis by linking institutional perspectives with technological accessibility and user behavior, thus facilitating the identification of trends and factors influencing the automation of financial services in the European Union.

Moreover, the selection of independent and control variables is grounded on theoretical and empirical contributions to the digitalization and technological transformation of the banking sector. Automation through mechatronic systems, as reflected in the number of ATMs, is a measure of the expansion of banks’ automated physical infrastructure, with direct implications for the operational efficiency and accessibility of banking services. In addition to this, artificial intelligence-based solutions are integrated through the DESI–Integration of Digital Technology (IDT) indicator, which reflects the degree of digitalization of companies, the DESI–Human Capital (HC) indicator, which measures the digital skills of the population, and the percentage of internet users using internet banking services, which indicates the level of adoption of digital technologies among consumers. These variables are essential to capture both the availability of digital infrastructure and the ability of users to adopt AI-based solutions in everyday financial activities.

As the process of innovation and technological adaptation is significantly influenced by the level of investment in research and development, the proposed model also incorporates R&D expenditure (R&D expenditure, by sectors of performance), a variable that indicates the level of investment in innovation and the ability of the financial sector to adopt emerging technologies. This variable helps to explain national differences in the adoption of emerging technologies, influencing both the automation of banking processes and the competitiveness of financial institutions. In addition to these explanatory variables, GDP per capita is used as a control variable, as it indirectly influences the level of digitalization and accessibility of financial technologies, and the number of employees in the banking sector allows for assessing the effects of automation on employment.

In Table 1, the H3 hypothesis is operationalized through the inclusion of two key variables. Firstly, R&D—Research and Development Expenditure: this variable is a direct proxy for national-level investment in innovation and technological advancement. It reflects the intensity of resources allocated to research and is measured as a percentage of GDP. Within the framework of H3, RD serves as the primary indicator of innovation input. Secondly, DESI_IDT—Digital Economy and Society Index–Integration of Digital Technology: this variable captures the degree to which businesses and the broader economy have integrated digital tools and infrastructure. It represents the initial level of digitalization mentioned in the hypothesis, reflecting a country’s readiness to absorb and effectively implement innovation. The DESI_IDT is thus the moderator in the hypothesized relationship. The interaction between these two variables is central to H3. Specifically, the hypothesis suggests that the effectiveness of R&D in enhancing banking sector performance (measured through ROE) is conditional on the digital maturity of the economy, as captured by DESI_IDT. This allows the model to assess whether innovation investments are more productive in digitally advanced environments compared to less developed ones. While ROE is the dependent variable through which this effect is ultimately measured, and thus essential to the empirical test of the hypothesis, RD and DESI_IDT are the variables most directly and conceptually tied to H3. No other variables in the table serve the same conceptual or operational role for this hypothesis.

Furthermore, the study incorporates instrumental variables represented by MOMENT1 and MOMENT2, in alignment with the MMQR framework as proposed by Machado and Santos Silva [115]. These variables are designed to capture unobservable processes and mitigate simultaneity bias, thereby enhancing the reliability of causal inference. Thus, the use of moment conditions in the MMQR framework addresses potential endogeneity and omitted variable bias while ensuring valid inference across conditional quantiles of ROE.

The selection of return on equity (ROE) as the primary dependent variable is rooted in its direct relevance to shareholder value and strategic banking decisions. Unlike Return on Assets (ROA), which captures operational efficiency, ROE integrates both profitability and leverage, providing a more comprehensive reflection of performance outcomes influenced by technological and organizational transformations, especially those tied to automation and digitalization. Net Interest Margin (NIM), while valuable, is more sensitive to monetary policy and interest rate environments, and less so to operational or digital efficiency. Our conceptual framework, aligned with the recent literature (e.g., [59,60]), prioritizes ROE due to its sensitivity to structural shifts in bank strategy catalyzed by automation technologies.

Concerning data transparency and cleaning procedures, specifically, we conducted rigorous outlier detection, inputting minimal missing values using panel-consistent mean imputation for time-invariant gaps, and excluded cross-sections with missingness above a defined threshold. All sources—IMF, Eurostat, and ECB—were cross-referenced to ensure consistency across units and temporal coverage. Also, data underwent standard cleaning procedures prior to estimation, including log-transformation for normalization, visual inspection for outliers, and listwise deletion of incomplete cases.

As automation decisions are often made at the firm level, the absence of harmonized bank-level data across EU member states necessitates the use of national proxies. As argued in Wisskirchen et al. [2] and Ruiz et al. [40], national digital indices are legitimate proxies when firm-level heterogeneity is either absorbed through fixed effects or when the objective is to evaluate systemic readiness and macroeconomic impacts. Moreover, robustness checks using cross-sectional heteroskedasticity and fixed effects estimations further ensure that aggregated data do not distort inference.

Thus, regarding the quantification of digitalization and automation, the study innovatively leverages proxy indicators derived from authoritative sources:

• Chatbot adoption and AI-based tools are proxied through DESI Human Capital (HC) and DESI Integration of Digital Technology (IDT) indices, both of which capture firm-level and infrastructure readiness for advanced digital implementation across financial services;
• Digital inclusion (DIGINCL) and E-banking usage (EBANK) serve as downstream indicators of AI diffusion in customer-facing services, which inherently include conversational banking interfaces;
• While direct bank-level adoption thresholds for chatbots are not publicly available in a cross-national format, our methodology mirrors recent practices in automation impact studies [33,40], where national-level digital readiness serves as a valid proxy for sectoral technology penetration.

Automation employment implications are operationalized through the variable BANKEMPL, representing the number of employees in the banking sector. Changes in this variable over time, controlled alongside other automation indicators, enable us to infer the structural impact of automation. While detailed job role classification was beyond this study’s scope, this indicator remains consistent with macro-level analyses of digital labor substitution in finance [38,61].

The methodology applied in this study follows a structured and rigorous approach to statistical and econometric analysis, ensuring the reliability and comprehensiveness of the findings regarding the impact of automation on banking performance. Each methodological step is carefully selected to mitigate potential biases, validate estimations, and capture the complex interdependencies inherent in the dataset.

Analyzing the impact of robotic automation and artificial intelligence-based solutions on the performance of the banking sector in the European Union requires a robust methodological approach capable of capturing the conditional distribution of the effects of these technologies on return on equity (ROE). In this context, the use of MMQR, following the approach proposed by Machado and Santos Silva [115], allows for capturing the heterogeneity of the impact of the explanatory variables on bank performance, offering a superior alternative to the classical least squares (OLS) regression models, which only estimate the average effects of the independent variables on ROE, without capturing possible differences in the impact on lower performing banking institutions compared to the more profitable ones. Given that AI technologies and ATM automation can have asymmetric effects on banks with low, medium, or high profitability, MMQR is preferred because it provides a robust and detailed estimate of the relationships between variables across the conditional distribution of profitability.

The benchmark econometric model is expressed following the approach of Machado and Santos Silva [115]:

$$\begin{gathered} RO{E}_{i,t}={\beta }_0+{\beta }_{1}AT{M}_{i,t}+{\beta }_{2}DESI\_ID{T}_{i,t}+{\beta }_{3}DESI\_H{C}_{i,t}+{\beta }_{4}E{BANK}_{i,t}+ \\ {\beta }_5{DIGINCL}_{i,t}+{\beta }_{6}R{D}_{i,t}+{\beta }_{7}GD{P}_{i,t}+{\beta }_{8}BANKEMP{L}_{i,t}+{ϵ}_{i,t} \end{gathered}$$

(1)

where $RO{E}_{i,t}$ is the return on equity for the country $i$ at time $t$, and ${ϵ}_{i,t}$ is the error term.

This equation represents the baseline panel regression model estimating the effects of automation and digitalization variables on return on equity (ROE), which is the primary performance indicator for banking institutions. Subscripts $i$ and $t$ denote country and time (year), respectively.

On the left-hand side, ROE_i,t is the dependent variable.

The right-hand side includes the following independent variables:

• ATM_i,t: density of automated teller machines (a mechatronic proxy);
• DESI_IDT_i,t and DESI_HC_i,t indicators of digital integration and human capital;
• EBANK_i,t and DIGINCL_i,t: digital financial service usage and inclusion;
• RD_i,t: R&D expenditures;
• GDP_i,t: economic growth;
• BANKEMPL_i,t: number of employees in the banking sector;
• ε_i,t captures the unobserved error term.

Instead of estimating this model using the least squares (OLS) method, MMQR seeks to estimate conditional quantile functions for different values of $\tau$, where τ ∈ (0,1) defines various points in the conditional distribution of ROE. Thus, the model becomes the following:

$$Q_{ROE}\left(\mathsf{\tau }|X\right)=\mathsf{\mu }\left(X\right)+\mathsf{\sigma }\left(X\right)\cdot q_U\left(\mathsf{\tau }\right)$$

(2)

where $Q_{ROE}\left(\mathsf{\tau }|X\right)$ is the quantile $\tau -a$ of the conditional distribution of bank ROE, $\mathsf{\mu }\left(X\right)$ and $\mathsf{\sigma }\left(X\right)$ being functions of the independent variables that control the location and dispersion of ROE. The error distribution is modeled to respect the conditions of the moments specific to quantile regression, following the approach of Machado and Santos Silva [115]. It expresses the conditional quantile function of ROE given the covariates X.

• $Q_{ROE}\left(\mathsf{\tau }|X\right)$ is the τ-th quantile (e.g., 25th, 50th, 75th, or 90th percentile) of the conditional distribution of ROE;
• $\mathsf{\mu }\left(X\right)$ denotes the location function, reflecting how the center of the conditional distribution shifts with changes in X;
• $\mathsf{\sigma }\left(X\right)$ represents the scale function, capturing heteroskedasticity or how dispersion in ROE changes with covariates;
• $q_U\left(\mathsf{\tau }\right)$ is the τ-th quantile of a standard uniform error term U, which introduces non-linearity into the model.

This equation enables us to model distributional heterogeneity, revealing how the effects of covariates vary across different performance levels of banks.

Thus, the application of the MMQR method for analyzing the impact of robotic automation and AI solutions in the banking sector involves several essential methodological steps.

First, it is necessary to prepare the dataset, which includes the collection of information on the number of ATMs, DESI indicators, proportion of internet banking users, R&D expenditure, GDP per capita, and number of employees in the banking system for each EU country in the period of 2017–2022. The variables are checked for outliers, missingness, and multicollinearity, and logarithmic transformations are applied to normalize the distributions where necessary.

Second, the MMQR model is estimated to use relevant quantiles such as τ = 0.25, 0.50, 0.75, and 0.90 to capture the effects on low, medium, and high profitability banks. For each quantile, we solve the system of equations based on the moment conditions:

$$E\left[\mathsf{\psi }\left(Y,X;\mathsf{\theta }\right)\right]=0,\hspace{1em}E\left[\mathsf{\varphi }\left(Y,X;\mathsf{\theta }\right)\right]=0$$

(3)

where $\mathsf{\theta }$ is the vector of the model’s estimator coefficients, and $\mathsf{\psi }$ and $\mathsf{\varphi }$ are functions defined based on the distribution of model errors. Equation (3) represents the moment conditions central to the estimation of MMQR. These expectations imply that the estimators are derived from a system of equations where the weighted residuals of the model have a mean of zero, satisfying the generalized method of moments (GMM) framework. ψ(Y,X;θ) and ϕ(Y,X;θ) are moment functions that depend on the observed outcome Y, covariates X, and parameters θ. The moment conditions ensure identification and consistency of the estimators by leveraging distributional information beyond the conditional mean, accommodating fixed effects, endogeneity, and heteroskedasticity in the data. These moment conditions underpin the MMQR approach, allowing for robust quantile-specific inference in a heterogeneous panel setting.

Next, it is essential to check the validity of the model by a series of statistical tests. Heteroscedasticity testing is performed using the Breusch–Pagan test, to confirm that the dispersion of errors varies across quantiles. The Wald test is also performed to check the significance of the coefficients at different quantiles.

Ultimately, the results are analyzed comparatively across quantiles to identify whether the impact of bank automation and AI-driven solutions in banking differs across banks with different performance levels. The interpretation of the MMQR coefficients allows us to understand how automation and AI-driven solutions influence the financial stability of banks and whether these technologies contribute to improving profitability in a uniform or differentiated way depending on the performance of the analyzed institutions.

The adoption of the Method of Moments Quantile Regression method in this context provides an advanced perspective on the impact of automation and AI on the European banking sector, overcoming the limitations of classical methods through a distributed analysis of the effects across different performance levels. As the integration of advanced technologies may have diverging effects on banking institutions with low profitability compared to more profitable ones, this methodology allows a more nuanced understanding of the phenomenon and supports the formulation of effective policies to adapt the banking system to the emerging technological transformations.

The analysis begins (Figure 2) with an examination of descriptive statistics, which establishes the foundational understanding of the dataset by summarizing central tendencies, dispersion, and distribution patterns. This preliminary investigation is essential for detecting trends, potential outliers, and anomalies that could influence subsequent econometric modelling. By providing insights into variability and skewness, this stage informs the selection of appropriate econometric techniques, thereby enhancing the robustness of the study.

Given the panel structure of the dataset, the next stage involved testing for cross-sectional dependency, following the methodologies proposed by Pesaran [116] and Fan et al. [117]. Since banks are often influenced by common external shocks, regional dynamics, or global technological advancements, neglecting such dependencies could result in biased and inconsistent estimates. Addressing this issue ensures that the estimation process accounts for the interconnected nature of the observations, thereby increasing the validity of the empirical findings.

Ensuring the reliability of time-series data requires an evaluation of stationarity properties, which is conducted using the unit root tests proposed by Im, Pesaran, and Shin [118]. Establishing whether variables exhibit mean-reverting behavior or stochastic trends is significant for preventing spurious regressions, where observed correlations between variables might simply reflect common trends rather than genuine economic relationships. Addressing stationarity issues strengthens the credibility of the subsequent econometric inferences.

To ascertain the presence of stable long-term relationships among the variables, the study applies cointegration tests based on the methodologies of Kao [119] and Johansen [120]. Establishing cointegration confirms whether variables exhibit a shared trajectory over time, despite potential short-term fluctuations. This step is particularly relevant in the context of automation, where the economic effects of technological adoption may evolve gradually rather than manifest immediately. Recognizing these long-run relationships provides a more comprehensive understanding of the sustained impact of banking automation on banking performance.

Furthermore, the study examines slope heterogeneity using the framework developed by Pesaran and Yamagata [121]. This methodological refinement determines whether the relationships between banking automation and banking performance vary significantly across different national contexts. The presence of heterogeneous slopes suggests that the impact of banking automation is shaped by country-specific characteristics such as economic performance, digital maturity, and integration of technology. By incorporating this aspect into the analysis, the study provides a differentiated understanding of how automation influences banking performance across diverse economies.

To capture heterogeneity and distributional effects within the data, the study employs the Method of Moments Quantile Regression (MMQR), as introduced by Machado and Santos Silva. Unlike conventional regression techniques that focus on mean effects, MMQR provides a nuanced analysis by examining the impact of banking automation across different quantiles of the dependent variable. This approach enables a deeper exploration of how banking automation affects banks with varying levels of economic performance, from low-productivity countries to highly advanced ones. Moreover, MMQR effectively addresses endogeneity concerns, ensuring that the estimated coefficients reflect causal relationships rather than spurious correlations.

Machado and Silva [115] introduced an advanced quantile regression methodology that effectively captures the conditional distribution effects of robotic automation in EU countries while incorporating fixed effects, heterogeneity, and endogeneity within panel data structures. By extending beyond the conventional panel quantile regression approaches developed by Koenker and Bassett Jr. [122], this method, known as the Method of Moments Quantile Regression with Fixed Effects (MMQR), provides a more comprehensive framework for analyzing the heterogeneous relationships between robotic automation, digitalization, and return on equity. Unlike earlier techniques, MMQR is specifically designed to accommodate panel data heterogeneity, allowing for a more precise estimation of the conditional covariance effects of robotic automation and other explanatory variables.

An essential feature of MMQR lies in its ability to account for both fixed effects and endogeneity, making it particularly well-suited for panel data models where regressors exhibit heterogeneous influences. By addressing these complexities, the method ensures that the estimated quantile functions remain well-ordered, thereby resolving a fundamental limitation of earlier quantile regression approaches. Moreover, by incorporating non-crossing constraints, MMQR preserves the logical structure of the conditional distribution, ensuring robustness in the estimation process.

The application of the Method of Moments Quantile Regression (MMQR) in this study is justified by its ability to capture the heterogeneous effects of economic and digitalization indicators on banking profitability across different levels of the return on equity (ROE) distribution. Unlike traditional Ordinary Least Squares (OLS) regression, which focuses on estimating the conditional mean relationship, MMQR provides a more comprehensive understanding of the distributional impacts of explanatory variables. This approach is particularly relevant in banking profitability analysis, where the determinants of ROE may exert varying influences depending on the financial health and competitive positioning of banks.

A key motivation for employing MMQR lies in the recognition that banking performance is not uniformly affected by macroeconomic and sectoral factors. Banks operating in different performance quantiles—low, median, and high profitability—face distinct constraints and opportunities, necessitating an analytical framework that accommodates this variation. By estimating the effects at multiple quantiles, MMQR allows for the identification of asymmetric relationships that would otherwise be obscured in standard regression models. For instance, economic growth or digital inclusion may have a stronger impact on highly profitable banks compared to those struggling with inefficiencies and regulatory burdens. Moreover, MMQR is robust to outliers and non-normal error distributions, making it particularly suited for financial datasets that often exhibit skewness and heavy-tailed distributions.

Furthermore, the MMQR framework incorporates higher-order moments, namely Moment 1 (MOMENT1) and Moment 2 (MOMENT2), which enhance its ability to capture unobserved heterogeneity and improve the efficiency of estimations. These moments represent the first and second derivatives of the quantile regression process, respectively.

• Moment 1 (MOMENT1) corresponds to the first moment of the distribution, capturing the linear component of the variation across quantiles. It reflects how the dispersion of profitability outcomes evolves as one moves from lower to higher quantiles. A significant MOMENT1 coefficient suggests that the impact of explanatory variables changes progressively along with the profitability distribution.
• Moment 2 (MOMENT2) represents the second moment of the distribution and captures the curvature or non-linear variation in the quantile regression process. This higher-order moment provides insights into whether the relationship between predictors and banking profitability is concave or convex, offering a deeper understanding of the underlying structural dynamics. A significant MOMENT2 coefficient indicates that the marginal effect of an explanatory variable is not constant across quantiles but instead exhibits increasing or decreasing returns.

The inclusion of these moments is significant for addressing potential issues related to heteroskedasticity and omitted variable bias, ensuring that the MMQR estimations remain robust and efficient. By leveraging these methodological advantages, this study provides a nuanced assessment of how macroeconomic and digitalization variables interact with banking profitability in the EU, offering policy-relevant insights that extend beyond conventional regression approaches.

To enhance the robustness of our empirical framework and to address the concerns related to residual heteroskedasticity and potential model misspecification, we complemented the MMQR with Panel Estimated Generalized Least Squares (EGLS). The EGLS technique, as outlined by Levin et al. [123] and Greene [124], is specifically tailored for panel data and corrects for cross-sectional and period-specific heteroskedasticity, offering efficiency gains over standard OLS and Fixed Effects estimation in the presence of non-spherical error terms.

In our model, we applied two EGLS variants:

• Cross-section weighted EGLS, which adjusts for heteroskedasticity between countries (i.e., structural differences in variance across banking systems).
• Period-weighted EGLS, which corrects for time-related volatility and ensures that shocks or structural changes during specific years (e.g., digital regulation peaks, pandemic effects) do not bias the estimates.

The integration of instrumental variables, robust estimation techniques, and extensive diagnostic testing collectively demonstrates that the model successfully mitigates endogeneity concerns and delivers stable and reliable estimates of the effects of automation technologies on banking performance.

4. Results

The trends observed across EU countries from 2017 to 2022 according to the Eurostat Database [107] reveal significant disparities in banking performance, digitalization, and technological adoption, reflecting the broader economic and financial dynamics within the region. Return on equity (ROE) [106], a key measure of banking performance, displays divergent trends across member states. While countries such as Hungary, the Czech Republic, and Romania exhibit consistently high ROE values, indicative of strong profitability in their banking sectors, others, including Germany, France, and Denmark, maintain lower but more stable figures, suggesting a preference for risk-averse banking strategies. Notably, economies like Greece and Cyprus experienced periods of negative ROE, highlighting structural vulnerabilities and financial sector challenges.

The penetration of internet banking has followed an upward trajectory across all EU nations [107], albeit at different speeds. In Northern and Western European economies, such as Finland, the Netherlands, and Denmark, the proportion of individuals using internet banking was already high in 2017 and has since reached near saturation. Conversely, in Southern and Eastern European countries, including Bulgaria, Romania, and Greece, the growth rate has been more pronounced, reflecting rapid improvements in digital infrastructure and financial inclusion. However, despite this progress, these countries continue to lag behind their Northern and Western counterparts, illustrating persistent disparities in digital financial access.

A similar trend is evident in digital inclusion [107], measured through the frequency of internet access. Countries with higher levels of digital penetration, such as Sweden, Finland, the Netherlands, Luxembourg, and Denmark exhibit strong digital engagement, reinforcing their status as leaders in financial digitalization. In contrast, countries like Italy and Portugal have demonstrated slower adoption rates, pointing to gaps in digital literacy and infrastructure that may impede the transition to fully digital banking systems.

The decline in the number of automated teller machines (ATMs) [107] across most EU countries signals a shift toward cashless transactions and digital banking solutions. Countries such as Belgium, Spain, and France have experienced the most significant reductions in ATMs, aligning with their strong adoption of mobile banking and contactless payment methods. In contrast, Bulgaria, Romania, and Slovakia have maintained relatively stable ATM numbers, suggesting that traditional banking services continue to play an essential role in these economies.

The Digital Economy and Society Index (DESI)—Integration of Digital Technology exhibits a consistent upward trend across the EU [108], yet with varying intensities. Countries such as Poland, Italy, and Spain have recorded some of the most substantial improvements, reflecting national efforts to promote digital transformation in business and finance. By contrast, economies such as Germany and Luxembourg, despite already possessing advanced digital infrastructures, have demonstrated more moderate growth in digital integration, suggesting that they have reached a point of technological maturity.

The relationship between GDP per capita [107] and banking digitalization further underscores the economic divide within the EU. Advanced economies, including Germany, the Netherlands, and Sweden, maintain high GDP per capita and strong digital banking infrastructures, reinforcing the link between economic prosperity and financial innovation. Conversely, lower-income economies, such as Bulgaria and Romania, display lower GDP per capita alongside slower progress in digital banking adoption, highlighting the financial constraints that may limit technological investment.

Research and development (R&D) expenditure [107] varies significantly across EU countries, reflecting different levels of commitment to financial innovation. Northern and Western European nations, particularly Sweden and Finland, Germany, Austria, and Belgium exhibit the highest levels of R&D investment, emphasizing their focus on fostering technological advancements in banking. In contrast, Southern and Eastern European countries, including Portugal and Greece, allocate fewer resources to R&D, suggesting that financial sector innovation remains a secondary priority.

The number of employees in the banking sector [109] provides additional insights into the impact of digital transformation on the labor market. Countries with advanced automation and AI-driven banking solutions, such as the Netherlands and Germany, have experienced a gradual reduction in banking employment, reflecting a shift towards digital-first financial services. Meanwhile, in countries where banking digitalization is still evolving, such as Hungary and Slovakia, employment levels have remained more stable, indicating a slower transition to automated banking solutions.

Lastly, the Digital Economy and Society Index (DESI)—Human Capital component [108] further illustrates the digital divide across the EU. Countries with highly skilled digital workforces, including Sweden and Finland, show strong banking automation adoption, reinforcing the importance of human capital in financial digitalization. Conversely, economies such as Italy and Greece demonstrate lower DESI human capital scores, reflecting the need for greater investment in digital education and workforce upskilling.

These trends collectively highlight the varying speeds of digital banking transformation across EU countries, with Northern and Western European economies leading the transition, while Southern and Eastern European nations work towards catching up. The observed disparities emphasize the necessity for targeted policy measures that support digital financial inclusion, investment in R&D, and the development of digital banking competencies across the region.

The statistical analysis (

Table 2. Summary statistics.

	LOG ROE	LOG ATM	LOGBANKEMPL	LOGDESI_HC	LOGDESI_IDT	LOGDIGINCL	LOGEBANK	LOGGDP	RD	MOMENT1	MOMENT2
Mean	1.956	8.333	10.490	2.415	3.310	4.437	3.985	4.568	1.089	0.328	−0.145
Median	2.104	8.505	10.570	2.419	3.342	4.456	4.113	4.532	0.900	0.126	0.018
Max	2.753	10.974	13.300	2.881	4.079	4.584	4.550	5.587	2.560	6.990	1.751
Min	−1.203	5.278	8.501	1.927	2.314	4.107	1.702	3.951	0.140	4.93 × 10−32	−18.483
Std.Dev	0.587	1.491	1.230	0.210	0.385	0.099	0.564	0.334	0.685	0.781	1.870
Skew.	−2.288	0.044	0.386	−0.172	−0.461	−0.924	−2.222	0.991	0.607	6.063	−7.803
Kurtos.	11.01	2.318	2.656	2.536	2.789	3.707	8.165	4.309	2.141	45.370	69.794
Jar-Bera	528.7	2.934	4.435	2.073	5.555	24.355	288.255	35.058	13.73	12058.3	29210.8
Prob.	0.000	0.230	0.108	0.354	0.062	0.000	0.000	0.000	0.001	0.000	0.000
Sum	291.5	1241.7	1563.129	359.959	493.261	661.168	593.813	680.64	162.2	48.915	−21.616
Obs.	149	149	149	149	149	149	149	149	149	149	149

Source: Author’s own calculation.

) of the provided dataset highlights key trends in banking performance, digitalization, and economic conditions across the European Union. Return on equity (ROE) demonstrates a moderate mean value, though the distribution is highly skewed, indicating significant variations across observations. The negative skewness suggests that some countries or periods experienced lower profitability, potentially linked to economic downturns or structural inefficiencies in banking operations. The high kurtosis further indicates the presence of extreme values, emphasizing the need for robust econometric techniques to account for such heterogeneity.

The variable representing research and development (R&D) expenditure exhibits a relatively low mean and a broad dispersion, suggesting that investment in financial sector innovation remains unevenly distributed across the EU. The positive correlation with GDP per capita implies that economies with higher income levels allocate more resources to R&D, reinforcing the notion that economic development fosters technological progress and banking innovation. However, the weak correlation with ROE suggests that, at least in the short term, increased R&D spending does not necessarily translate into immediate profitability gains for banks.

The log-transformed GDP per capita (LOGGDP) presents a strong positive association with digital banking variables, particularly Digital Inclusion (LOGDIGINCL) and E-banking adoption (LOGEBANK). This result confirms the widely accepted hypothesis that wealthier economies exhibit higher levels of digital financial integration. Moreover, the significant correlation between GDP per capita and DESI indicators reflects the broader impact of economic development on digital transformation, with countries investing more in technology benefiting from higher adoption rates of AI-driven banking solutions.

The negative correlation between ROE and digital banking indicators, including E-banking penetration (LOGEBANK), Digital Inclusion (LOGDIGINCL), and DESI-Integration of Digital Technology (LOGDESI_IDT), suggests that increased digitalization does not necessarily lead to higher banking profitability in the short term. This may be attributed to the significant transition costs associated with digital transformation, as banks invest heavily in automation, cybersecurity, and infrastructure before realizing efficiency gains. Additionally, increased competition from digital-first financial institutions may compress traditional banks’ profit margins, leading to lower ROE despite technological advancements.

The number of automated teller machines (LOGATM) is negatively correlated with digital banking indicators, confirming the decline of physical banking infrastructure as digital transactions become more prevalent. The strong inverse relationship with E-banking adoption and DESI-Integration of Digital Technology suggests that as online and mobile banking solutions gain traction, reliance on ATMs diminishes. However, the lack of a significant correlation with ROE implies that the reduction in ATM usage does not directly impact banking profitability, possibly due to cost-saving measures that offset the decline in transaction-based revenues.

An important observation from the Jarque–Bera test results is that the ROE distribution deviates significantly from normality, as evidenced by the extremely low p-value. Similar results are obtained for variables such as E-banking adoption and GDP per capita, suggesting that conventional econometric methods assuming normality may not be appropriate for analyzing these relationships. The presence of skewness and kurtosis in key variables underscores the need for methodologies like Method of Moments Quantile Regression (MMQR), which can account for distributional heterogeneity and capture nuanced effects across different quantiles of the banking performance distribution.

The covariance analysis (

Table 3. Correlation analysis.

Correlation
Probability	LOG ROE	LOG ATM	LOGBANK EMPL	LOGDESI_HC	LOGDESI_IDT	LOGDIGINCL	LOG EBANK	LOGGDP	RD	MOM1	MOM2
LOGROE	1.0000
	-----
LOGATM	0.0017	1.0000
	0.9836	-----
LOGBANK EMPL	−0.1173	0.9174	1.0000
	0.1540	0.0000	-----
LOGDESI_HC	−0.1022	−0.3106	−0.1383	1.0000
	0.2147	0.0001	0.0925	-----
LOGDESI_IDT	−0.1863	−0.1773	−0.0168	0.811365	1.0000
	0.0229	0.0304	0.8383	0.0000	-----
LOG DIGINCL	−0.0784	−0.2535	−0.0243	0.7580	0.7328	1.0000
	0.3416	0.0018	0.7680	0.0000	0.0000	-----
LOG EBANK	−0.1015	−0.2462	−0.0981	0.7750	0.7381	0.8208	1.0000
	0.2179	0.0025	0.2335	0.0000	0.0000	0.0000	-----
LOGGDP	−0.2406	−0.1575	0.1521	0.6461	0.5794	0.6548	0.5247	1.0000
	0.0031	0.0551	0.0640	0.0000	0.0000	0.0000	0.0000	-----
RD	0.0677	0.2777	0.3905	0.4913	0.4953	0.5038	0.4375	0.4072	1.0000
	0.4119	0.0006	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	-----
MOMENT1	−0.5693	−0.1020	−0.0699	−0.0758	0.1059	−0.0047	0.0143	−0.0532	−0.1168	1.0000
	0.0000	0.2155	0.3969	0.3578	0.1983	0.9543	0.8618	0.5186	0.1558	-----
MOMENT2	0.7121	0.1534	0.1164	0.0126	−0.0109	0.0202	0.0304	0.0139	0.1125	−0.8741	1.0000
	0.0000	0.0617	0.1574	0.8784	0.8943	0.8062	0.7128	0.8658	0.1716	0.0000	-----

Source: Author’s own calculation.

) further supports the existence of significant relationships among digitalization, economic development, and banking automation.

While high correlations between GDP per capita, digital banking, and DESI indicators validate the role of economic strength in technological adoption, the lack of strong positive correlations between digital transformation and ROE suggests that banking profitability remains influenced by other factors, including regulatory environments, operational efficiencies, and market competition.

These results collectively highlight the complexities of digital transformation in the banking sector. While technological advancements and automation improve accessibility and efficiency, their immediate impact on banking profitability is less evident, requiring further investigation into the long-term effects. The findings emphasize the importance of policy measures that support both innovation and financial stability, ensuring that digital banking transformation benefits all stakeholders within the European Union’s financial landscape.

The analysis of the descriptive statistics and covariance results offers a nuanced perspective on the relationship between key banking performance indicators, macroeconomic variables, and digital transformation across EU countries. The distribution of return on equity (ROE), as captured by the mean and median values, highlights the central tendency of banking profitability, while the negative skewness and high kurtosis suggest the presence of extreme low-profitability observations. The dispersion of ROE, as evidenced by the standard deviation, reflects heterogeneity in banking performance among banks, possibly due to variations in digital adoption, economic conditions, and market structures.

Examining the digitalization indicators—Digital Inclusion (LOGDIGINCL), Human Capital (LOGDESI_HC), and Integration of Digital Technologies (LOGDESI_IDT)—reveals their role in shaping banking outcomes. Although these variables exhibit moderate variability, their correlation structure indicates strong interdependence. Notably, LOGDESI_IDT and LOGDESI_HC are highly correlated, suggesting that investments in digital infrastructure and human capital complement each other in fostering financial sector efficiency. The negative relationship between ROE and LOGDESI_IDT, though statistically significant, suggests that a higher intensity of digital integration may not translate directly into immediate profitability gains. This could be attributed to the initial costs of digital transformation, regulatory adjustments, or the time lag required for digital investments to yield efficiency improvements.

The macroeconomic environment, as reflected by GDP (LOGGDP) and research and development (RD), also plays an important role in determining banking performance. The negative association between ROE and GDP, despite being significant, suggests that profitability dynamics are more complex than a simple pro-cyclicality assumption. While economic growth generally fosters banking sector expansion, competitive pressures, regulatory compliance costs, or structural banking inefficiencies in highly developed economies may suppress profit margins. Conversely, the positive relationship between RD and ROE suggests that innovation and technological advancement could serve as drivers of profitability, reinforcing the argument that investment in research fosters long-term financial sustainability.

The banking sector’s structural characteristics, represented by employment in banking (LOGBANKEMPL), electronic banking (LOGEBANK), and ATM density (LOGATM), provide further insights into the operational environment. The strong positive correlation between LOGBANKEMPL and LOGATM underscores the coexistence of traditional and digital banking infrastructures, where workforce intensity remains relevant despite increasing automation. The negative association of banking employment with ROE, though not statistically significant, aligns with the hypothesis that cost structures related to human resources may act as a drag on profitability in an era of increasing digitalization.

The inclusion of higher-order moments (MOMENT1 and MOMENT2) within the MMQR framework adds a significant dimension to the interpretation of these relationships. The negative correlation between MOMENT1 and ROE suggests that variations in profitability across quantiles follow a nonlinear trajectory, with lower quantiles experiencing greater instability. The highly significant positive correlation between MOMENT2 and ROE confirms that banking performance exhibits increasing dispersion across quantiles, implying that banks at the upper end of the profitability distribution respond differently to the explanatory variables compared to their lower-performing counterparts.

The Jarque–Bera test statistics and the associated probabilities reinforce the presence of non-normal distributions, particularly for ROE, GDP, and digitalization indicators. This further justifies the choice of MMQR as an analytical tool, given its robustness regarding deviations from normality and ability to accommodate heteroskedasticity. The overall interpretation of the findings suggests that while digitalization and macroeconomic variables exert notable influences on banking profitability, their effects are contingent on quantile positioning, operational structures, and the prevailing economic environment in EU countries.

The results of the Kao Residual Cointegration Test (

Table 4. Kao cointegration test.

	t-Statistic	Prob.
ADF	−6.936554	0.0000
Residual variance	0.120619
HAC variance	0.073685
ADF Test Equation Dependent Variable: D(RESID) R-squared: 0.562447 Adjusted R-squared: 0.562447	Coefficient −1.140605	Prob. 0.0000

Source: Author’s own calculation.

) confirm the presence of a long-run equilibrium relationship among the variables, rejecting the null hypothesis of no cointegration with a statistically significant ADF t-statistic −6.936554, p = 0.0000). This suggests that return on equity (ROE), research and development expenditure (RD), GDP per capita (GDP), E-banking adoption (EBANK), digital inclusion (DIGINCL), and digital economy indicators (DESI_IDT, DESI_HC), along with bank employment (BANKEMPL) and the number of ATMs (ATM), exhibit a stable long-term association. Such cointegration implies that these variables do not drift apart over time and are influenced by common structural factors shaping financial and digital economic performance in the European Union.

Further validation through the Augmented Dickey–Fuller (ADF) test on the residuals strengthens the evidence for cointegration, as the negative and significant coefficient of RESID(−1) −1.140605, p = 0.0000) indicates that deviations from equilibrium are corrected over time. The high adjusted R-squared (0.562447) suggests that a substantial proportion of variations in the dependent variable (changes in residuals) can be explained by past disequilibria, reinforcing the strength of the long-run relationships.

The Johansen Cointegration Tests (

Table 5. Johansen Fisher panel cointegration test.

Unrestricted Cointegration Rank Test (Trace and Maximum Eigenvalue)
Hypothesized	Fisher Stat.		Fisher Stat.
No. of CE(s)	(from trace test)	Prob.	(from max-eigen test)	Prob.
None *	446.6301	0.0000	106.8879	0.0000
At most 1 *	339.7421	0.0000	84.63178	0.0002
At most 2 *	255.1104	0.0000	74.45463	0.0007
At most 3 *	180.6557	0.0021	56.01552	0.0202
At most 4	124.6402	0.0572	43.58368	0.0937

The Trace test indicates 4 cointegrating eqn(s) at the 0.05 level; the Max-Eigenvalue test indicates 4 cointegrating eqn(s) at the 0.05 level; * denotes rejection of the hypothesis at the 0.05 level. Source: Author’s own calculation.

), employing both the Trace and Maximum Eigenvalue tests, provide further confirmation of these long-run relationships. The Trace test indicates two cointegrating equations, while the Maximum Eigenvalue test suggests three cointegrating equations, rejecting the null hypothesis at the 5% level. These results imply that banking performance, digital transformation, and economic development are jointly determined by multiple stable relationships.

Turning to residual diagnostics (

Table 6. Residual diagnostics.

Panel Cross-Section Heteroskedasticity LR Test
	Value	df	Probability
Likelihood ratio	168.1047	27	0.0000

Source: Author’s own calculation.

), the likelihood ratio test for heteroskedasticity unequivocally rejects the null hypothesis of homoskedastic residuals across both cross-sections and time periods. This finding highlights structural differences across banking institutions and EU economies, suggesting that variability in banking profitability and digital transformation effects is contingent on country-specific factors. The significant log-likelihood differences between the restricted and unrestricted models confirm that failing to account for heteroskedasticity would lead to misleading inferences.

Examining the Panel Estimated Generalized Least Squares (EGLS) results (

Table 7. (a) Panel Estimated Generalized Least Squares (EGLS) Results—cross section weights. (b) Panel Estimated Generalized Least Squares (EGLS) Results—period weights. (c) Panel EGLS results (synthesis).

(a)
Variable		Coefficient	Std. Error	t-Statistic		Prob.
LOGATM		0.133626	0.030709	4.351367		0.0000
LOGBANKEMPL		−0.209911	0.042370	−4.954290		0.0000
LOGDESI_HC		1.096892	0.142832	7.679610		0.0000
LOGDESI_IDT		−1.169670	0.042611	−27.44990		0.0000
LOGDIGINCL		2.026711	0.133127	15.22389		0.0000
LOGEBANK		−0.249761	0.038688	−6.455854		0.0000
LOGGDP		−0.258883	0.064009	−4.044504		0.0001
RD		0.273297	0.012905	21.17785		0.0000
MOMENT1		0.650968	0.015505	41.98429		0.0000
MOMENT2		0.391258	0.006523	59.97923		0.0000
C		−2.864215	0.610652	−4.690419		0.0000
		Weighted Statistics
R-squared		0.977155	Mean dependent var			12.82729
Adjusted R-squared		0.975499	S.D. dependent var			34.05505
S.E. of regression		0.400361	Akaike info criterion			−0.457428
Sum squared resid		22.11988	Schwarz criterion			−0.235660
Log likelihood		45.07838	Hannan–Quinn criter.			−0.367327
F-statistic		590.2642	Durbin–Watson stat			2.028018
Prob(F-statistic)		0.000000
		Unweighted Statistics
R-squared		0.566544	Mean dependent var			1.956753
Sum squared resid		22.12014	Durbin–Watson stat			1.229024
(b)
Variable		Coefficient	Std. Error	t-Statistic		Prob.
LOGATM		0.188631	0.046479	4.058386		0.0001
LOGBANKEMPL		−0.323770	0.056521	−5.728280		0.0000
LOGDESI_HC		0.926947	0.202242	4.583349		0.0000
LOGDESI_IDT		−0.921624	0.096607	−9.539929		0.0000
LOGDIGINCL		2.296045	0.371943	6.173118		0.0000
LOGEBANK		−0.289611	0.065868	−4.396819		0.0000
LOGGDP		−0.281414	0.099263	−2.835022		0.0053
RD		0.245810	0.035356	6.952406		0.0000
MOMENT1		0.418840	0.048837	8.576286		0.0000
MOMENT2		0.428785	0.023229	18.45944		0.0000
C		−3.436383	1.451358	−2.367702		0.0193
		Weighted Statistics
R-squared		0.805073	Mean dependent var			3.186650
Adjusted R-squared		0.790948	S.D. dependent var			1.876816
S.E. of regression		0.360508	Akaike info criterion			0.228079
Sum squared resid		17.93530	Schwarz criterion			0.449847
Log likelihood		−5.991886	Hannan–Quinn criter.			0.318180
F-statistic		56.99586	Durbin–Watson stat			1.329893
Prob(F-statistic)		0.000000
		Unweighted Statistics
R-squared		0.648550	Mean dependent var			1.956753
Sum squared resid		17.93522	Durbin–Watson stat			1.830891
(c)
Variable	EGLS Cross-Section Coefficient	EGLS Cross-Section Std. Error	EGLS Cross-Section p-Value	EGLS Period Coefficient	EGLS Period Std. Error	EGLS Period p-Value
LOGATM	0.133626	0.030709	0.0000	0.188631	0.046479	0.0001
LOGBANKEMPL	−0.209911	0.04237	0.0000	−0.32377	0.056521	0.0000
LOGDESI_HC	1.096892	0.142832	0.0000	0.926947	0.202242	0.0000
LOGDESI_IDT	−1.16967	0.042611	0.0000	−0.921624	0.096607	0.0000
LOGDIGINCL	2.026711	0.133127	0.0000	2.296045	0.371943	0.0000
LOGEBANK	−0.249761	0.038688	0.0000	−0.289611	0.065868	0.0000
LOGGDP	−0.258883	0.064009	0.0001	−0.281414	0.099263	0.0053
RD	0.273297	0.012905	0.0000	0.24581	0.035356	0.0000
MOMENT1	0.650968	0.015505	0.0000	0.41884	0.048837	0.0000
MOMENT2	0.391258	0.006523	0.0000	0.428785	0.023229	0.0000
C	−2.864215	0.610652	0.0000	−3.436383	1.451358	0.0193

Source: Author’s own calculation.

a–c), the statistically significant coefficients provide deeper insights into the determinants of banking profitability. The positive and significant impact of digital inclusion on return on equity suggests that broader accessibility to digital financial services fosters banking performance. However, the negative coefficient associated with digital integration (DESI_IDT) indicates that extensive reliance on digital technologies does not uniformly enhance profitability, possibly due to implementation costs or transitional inefficiencies. Banking employment exerts a negative influence on profitability, supporting the argument that labor-intensive banking models may be less efficient in the context of increasing digital adoption.

The positive association between research and development and return on equity highlights the role of innovation in sustaining banking performance, reinforcing the idea that investment in technological progress is instrumental in fostering competitive advantages in the banking sector. The higher-order moments incorporated into the model reveal that profitability dispersion and its sensitivity to explanatory variables vary across institutions and economic environments, emphasizing the heterogeneous nature of banking sector performance in the EU.

The cross-section weighted model (Table 7a) achieved an exceptionally high R-squared value of 0.9772 and an adjusted R-squared of 0.9755, indicating a highly explanatory model. The F-statistic value of 590.26 with a p-value of 0.000 confirms the overall significance of the model. Importantly, the Durbin–Watson statistic of 2.03 suggest the absence of autocorrelation. Furthermore, the LR test for heteroskedasticity yielded a p-value of 0.0000, confirming the presence of cross-sectional heteroskedasticity, which justifies the use of EGLS with cross-section weights.

In parallel, the period-weighted specification (Table 7b) maintained strong performance, with an R-squared of 0.8051 and adjusted R-squared of 0.7910. The F-statistics remained significant (F = 56.99, p = 0.000), and the model converged after 11 iterations. The heteroskedasticity test again returned a p-value of 0.0000, validating the correction mechanism. Although the Durbin–Watson statistics dropped to 1.33, suggesting some residual autocorrelation, the model still effectively mitigates structural heteroskedasticity.

The use of panel EGLS (Table 7c) directly responds to the problem of cross-sectional and period-based heteroskedasticity. The likelihood ratio tests included in the analysis unequivocally reject the null hypotheses of homoskedasticity at the 1% level for both cross-sectional (p = 0.0000) and period-based (p = 0.0000) residuals. This demonstrates the presence of significant heteroskedasticity within the dataset, validating the application of generalized least squares estimators. The weighted estimation ensures more efficient and unbiased coefficient estimates under the assumption of heteroskedastic and contemporaneously correlated errors, thus adhering to the robustness requirements advocated by the reviewer.

Moreover, the panel EGLS results exhibit high levels of explanatory power (R² = 0.977 cross-section; R² = 0.805 period-based), and all key automation and digitalization variables retain statistical significance at conventional levels. This corroborates the strength and reliability of the findings, while the use of robust estimation methods further enhances the empirical validity of the causal inferences.

The residual diagnostics (Table 6) confirm significant heteroskedasticity across both dimensions (Likelihood Ratio Tests with p < 0.0001), thus validating our choice of EGLS as a remedial technique. The EGLS results yielded consistent and significant coefficients aligned with the MMQR estimations, particularly for variables such as LOGDESI_HC, LOGATM, and RD, thereby reinforcing the reliability of our findings across quantiles.

The cross-section weighted model (Table 7a) achieved an exceptionally high R-squared value of 0.9772 and an adjusted R-squared of 0.9755, indicating a highly explanatory model. This robustness check strengthens the credibility of our causal inferences, especially in light of the complex, multi-dimensional nature of automation and banking performance.

In conclusion, the application of both MMQR and EGLS not only strengthens our estimation strategy against violations of homoskedasticity and normality but also ensures that the core results remain robust and generalizable. These methods together serve the dual function of methodological rigor and empirical resilience.

The results from the panel unit root tests (

Table 8. Unit root tests.

Series	Levin, Lin & Chu Statistic/p-Value		Im, Pesaran and Shin Statistic/p-Value		ADF—Fisher Chi-Square Statistic/p-Value		PP—Fisher Chi-Square Statistic/p-Value
LOGATM	6.86189	1.0000	5.11722	1.0000	25.026	0.9997	41.418	0.8952
D(LOGATM)	−3.79458	0.0001	−2.34246	0.0096	63.2403	0.1366	73.5263	0.0263
LOGBANKEMPL	−1.39476	0.0815	2.67976	0.9963	27.0677	0.9992	44.3244	0.8233
D(LOGBANKEMPL)	−16.611	0.0000	−3.32252	0.0004	73.8404	0.0377	77.9552	0.0181
LOGDESI_HC	0.56464	0.7138	4.20747	1.0000	14.2987	1.0000	16.0837	1.0000
D(LOGDESI_HC)	−22.7581	0.0000	−5.99027	0.0000	102.514	0.0001	129.715	0.0000
LOGDESI_IDT	−1.70833	0.0438	3.74306	0.9999	28.5285	0.9983	56.6257	0.3773
D(LOGDESI_IDT)	−19.6752	0.0000	−6.88869	0.0000	112.115	0.0000	126.631	0.0000
LOGDIGINCL	−5.78302	0.0000	0.68373	0.7529	48.7197	0.6776	85.9135	0.0037
D(LOGDIGINCL)	−11.9798	0.0000	−3.86545	0.0001	82.6333	0.0044	95.0081	0.0003
LOGEBANK	−7.22874	0.0000	−0.2065	0.4182	63.8147	0.1696	108.319	0.0000
D(LOGEBANK)	−5.53938	0.0000	−4.68541	0.0000	97.9074	0.0001	120.296	0.0000
LOGGDP	−4.13119	0.0000	0.82608	0.7956	46.8881	0.7428	70.9023	0.0612
D(LOGGDP)	−6.49996	0.0000	−1.73962	0.041	60.9259	0.2409	75.2483	0.0296
LOGROE	−6.92928	0.0000	−0.70481	0.2405	50.973	0.3575	59.2718	0.1276
D(LOGROE)	−12.1483	0.0000	−3.313	0.0000	65.208	0.0205	66.7249	0.0151
RD	−6.62636	0.0000	0.69738	0.7572	43.9194	0.8346	60.4366	0.2547
D(RD)	−12.407	0.0000	−2.89915	0.0019	75.0955	0.0304	95.2513	0.0005
MOMENT1	−25.9586	0.0000	−2.62642	0.0043	61.232	0.0657	79.1544	0.0017
D(MOMENT1)	−11.6008	0.0000	−3.33753	0.0004	62.9049	0.0119	72.3037	0.0013
MOMENT2	−106.927	0.0000	−9.25402	0.0000	55.2905	0.1639	60.2819	0.077
D(MOMENT2)	−5.56651	0.0000	−1.1143	0.1326	43.311	0.3319	41.9486	0.3864

Source: Author’s own calculation.

) provide important insights into the stationarity properties of the examined time series, which is fundamental for ensuring robust econometric modelling and inference. The application of multiple tests, including Levin, Lin & Chu, Im, Pesaran & Shin, and Fisher-type ADF and PP tests, allows for a comprehensive assessment under different assumptions regarding the underlying data-generating processes.

At the level, the findings consistently indicate the presence of a unit root across most series. The p-values for Levin, Lin, & Chu [123], Im, Pesaran, & Shin [118], and the Fisher tests [124] remain significantly above conventional significance thresholds, suggesting that these series exhibit non-stationary behavior. For instance, the variable LOGATM demonstrates a p-value of 1.000 across most tests, reinforcing the hypothesis that it follows a unit root process.

Similarly, LOGBANKEMPL, LOGDESI_HC, and LOGDESI_IDT exhibit p-values close to one, confirming the inability to reject the null hypothesis of a unit root. However, there are exceptions where certain tests provide mixed evidence. For example, LOGDIGINCL and LOGEBANK show p-values below 0.05 under Levin, Lin, & Chu, indicating stationarity under the assumption of a common unit root process, whereas alternative tests such as those of Im, Pesaran, & Shin and Fisher-type methods suggest non-stationarity. This discrepancy underscores the importance of considering multiple tests, as differences in test power and assumptions may yield divergent conclusions.

Upon taking the first difference, the stationarity properties of the variables undergo a substantial transformation. Across all the examined series, the p-values for the Levin, Lin, & Chu, Im, Pesaran, & Shin, and Fisher tests decline sharply, overwhelmingly rejecting the null hypothesis of a unit root. For example, D(LOGATM) exhibits a p-value of 0.0001 under Levin, Lin, & Chu, while the Im, Pesaran, & Shin test confirms stationarity with a p-value of 0.0096. Similar results emerge for other transformed variables such as D(LOGBANKEMPL), D(LOGDESI_HC), and D(LOGDESI_IDT), all of which display highly significant test statistics, reinforcing their stationary nature after differencing. This consistent rejection of the unit root hypothesis at the first difference indicates that these series are integrated of order one, I(1), implying that they become stationary only after undergoing first differencing.

These findings have substantial implications for econometric analysis, particularly when estimating relationships between variables. The presence of unit roots at the level suggests that employing these variables in their original form could lead to spurious regressions, thereby compromising the validity of inference. Given that most series become stationary at first difference, models incorporating these variables should either utilize differenced data or employ cointegration techniques if long-run equilibrium relationships exist. The need for such transformations highlights the importance of stationarity tests in determining the appropriate methodological approach for empirical analysis.

Thus, the unit root test results confirm that the majority of the examined macroeconomic and financial time series are non-stationary in their level form but become stationary after first differencing. This evidence supports the classification of these variables as I(1) processes, necessitating appropriate econometric adjustments to avoid misleading statistical inferences.

The cross-section dependence tests (

Table 9. Cross-section dependence tests.

Series	Breusch–Pagan LM Statistic/p-Value		Pesaran Scaled LM Statistic/p-Value		Bias-Corrected Scaled LM Statistic/p-Value		Pesaran CD Statistic/p-Value
LOGATM	1261.915	0.0000	34.38028	0.0000	31.68028	0.0000	16.87518	0.0000
LOGBANKEMPL	1369.082	0.0000	38.42502	0.0000	35.72502	0.0000	16.76614	0.0000
LOGDESI_HC	1647.333	0.0000	48.92693	0.0000	46.22693	0.0000	40.23152	0.0000
LOGDESI_IDT	2016.091	0.0000	62.8448	0.0000	60.1448	0.0000	44.89023	0.0000
LOGDIGINCL	1352.775	0.0000	37.80957	0.0000	35.10957	0.0000	33.16456	0.0000
LOGEBANK	1442.69	0.0000	41.2032	0.0000	38.5032	0.0000	26.74926	0.0000
LOGGDP	774.432	0.0000	15.98141	0.0000	13.28141	0.0000	2.640705	0.0083
LOGROE	NA	NA	NA	NA	NA	NA	NA	NA
MOMENT1	NA	NA	NA	NA	NA	NA	NA	NA
MOMENT2	NA	NA	NA	NA	NA	NA	NA	NA
RD	899.6211	0.0000	20.70637	0.0000	18.00637	0.0000	12.91091	0.0000

Source: Author’s own calculation.

) reveal significant interdependencies among the panel data series, suggesting strong correlations across cross-sections. The results from the Breusch–Pagan LM test [125], Pesaran’s scaled LM test, the bias-corrected LM test, and the Pesaran CD test [117] consistently reject the null hypothesis of no cross-section dependence at conventional significance levels.

The Breusch–Pagan LM test statistics are notably large for all tested series, with p-values of 0.0000, confirming strong cross-sectional dependence. Similarly, Pesaran’s scaled LM and its bias-corrected version yield highly significant statistics across all series, reinforcing the conclusion that cross-sectional units are interrelated. The Pesaran CD test, which is particularly informative in large panels, also consistently rejects the null hypothesis, further substantiating the presence of correlation among cross-sectional units.

A particularly high degree of cross-section dependence is observed for variables such as LOGDESI_IDT, LOGDESI_HC, and LOGDIGINCL, where all test statistics reach exceptionally high values, indicating strong interlinkages between these series. LOGGDP presents a slightly different pattern, with the Pesaran CD test yielding a p-value of 0.0083, which, while still significant, suggests relatively lower cross-sectional dependence compared to other series.

For some series, such as LOGROE, MOMENT1, and MOMENT2, test statistics are unavailable, indicating potential data limitations or an unbalanced panel structure that precluded the computation of meaningful results.

These findings carry important econometric implications, particularly in terms of estimation techniques and model specification. The presence of cross-section dependence suggests that standard panel data estimators assuming independence across cross-sections may be inappropriate. Alternative approaches, such as common correlated effects estimators or spatial panel methods, should be considered to properly account for interdependencies. Furthermore, cross-section dependence may indicate the presence of common shocks or spillover effects across units, necessitating further investigation into potential underlying mechanisms driving these interrelations.

The results obtained through the Method of Moments Quantile Regression (MMQR) (

Table 10. MMQR Results.

Variable	Coefficient (Tau = 0.25)	Coefficient (Tau = 0.5)	Coefficient (Tau = 0.75)	Coefficient (Tau = 0.90)
LOGGDP	−0.256949 *	−0.239576 ***	−0.208465 *	−0.165777 *
	(0.0963)	(0.0162)	(0.0673)	(0.0810)
LOGEBANK	−0.321511	−0.274656 ***	−0.191033 ***	−0.156813 ***
	(0.1211)	(0.0000)	(0.0003)	(0.0001)
LOGDIGINCL	1.604587	2.250846 ***	1.627283 ***	1.579914 ***
	(0.2326)	(0.0000)	(0.0000)	(0.0000)
LOGDESI_IDT	−0.931122 ***	−0.755339 ***	−0.861912 ***	−0.924951 ***
	(0.0000)	(0.0000)	(0.0000)	(0.0000)
LOGDESI_HC	1.229662 **	0.526695 ***	0.658156 ***	0.688215 ***
	(0.0322)	(0.0168)	(0.0001)	(0.0000)
LOGBANKEMPL	−0.243212 ***	−0.259478 ***	−0.251788 ***	−0.27693 ***
	(0.0658)	(0.0000)	(0.0001)	(0.0000)
LOGATM	0.107644	0.14827 ***	0.13957 ***	0.159465 ***
	(0.3927)	(0.003)	(0.0049)	(0.0000)
RD	0.282881 ***	0.232124 ***	0.23456 ***	0.226394 ***
	(0.0166)	(0.0000)	(0.0000)	(0.0000)
MOMENT1	0.444957 ***	0.505355 ***	0.664001 ***	0.714017 ***
	(0.0000)	(0.0000)	(0.0000)	(0.0000)
MOMENT2	0.455733 ***	0.474509 ***	0.407429 ***	0.425924 ***
	(0.0000)	(0.0000)	(0.0000)	(0.0000)
C	−1.470964	−3.37703 ***	−1.013666	−0.882788
	(0.7881)	(0.0218)	(0.4016)	(0.2858)

Significance levels: *** 1%, ** 5%, * 10%. Parentheses contain p-values. Source: own processing.

) provide a comprehensive analysis of the determinants of return on equity (ROE) across different quantiles, capturing heterogeneity in banking performance across EU countries. This approach is particularly relevant for analyzing financial institutions in the European Union, as it allows for a deeper exploration of asymmetric effects and structural differences among banks operating under diverse economic, regulatory, and technological environments.

The influence of GDP on ROE is consistently negative across all quantiles, with stronger effects in the lower and middle quantiles. This suggests that in the countries analyzed, economic expansion does not uniformly translate into higher bank profitability, possibly due to structural inefficiencies or unequal growth distribution. The banking sector’s digitalization through internet banking use, captured by LOGEBANK, exhibits a significantly negative effect, particularly at higher quantiles, indicating that more profitable banks face diminishing returns from increased banking digitalization, potentially due to heightened competition or financial sector constraints on weak banks.

At the lower quantiles, specifically at the 0.25 quantile, the findings indicate that digitalization and financial innovation play an essential yet complex role in shaping banking performance. The negative and statistically significant coefficient of the logarithm of the design of inclusive digital transformation (LOGDESI_IDT) suggests that institutions in the lower quantile of ROE experience adverse effects from digital transformation strategies. This finding aligns with the idea that digital adaptation requires upfront investments and restructuring, which may not immediately translate into improved profitability, particularly for underperforming financial institutions. Conversely, the positive significance of the logarithm of human capital in digital design (LOGDESI_HC) underscores the importance of skilled labor in enhancing the success of digital strategies in the EU banking sector. The negative impact of the logarithm of banking employment (LOGBANKEMPL) further indicates that for banks with weaker banking performance, higher employment levels may reflect inefficiencies rather than value creation, a common challenge in institutions facing structural rigidities.

At the median quantile (0.50), the role of digitalization becomes more pronounced, with the logarithm of digital inclusion (LOGDIGINCL) emerging as a strong determinant of ROE. This suggests that financial institutions in the middle range of performance benefit significantly from digital accessibility and technological adoption. The continued negative effect of LOGDESI_IDT reinforces the argument that digital transformation strategies require time before yielding profitability, particularly in moderately performing banks. Meanwhile, the positive impact of research and development (RD) and higher-order moments (MOMENT1 and MOMENT2) underscores the relevance of innovation and financial stability in ensuring sustainable banking performance across EU institutions. The persistence of a negative effect of banking employment further suggests that in many EU countries, banking sector employment structures may require optimization to improve efficiency.

At higher quantiles, particularly at 0.75 and 0.90, the impact of digitalization becomes even more significant. LOGDIGINCL remains highly positive and statistically significant, highlighting that high-performing EU financial institutions derive substantial benefits from digital accessibility. This suggests that in more technologically advanced financial systems, digital infrastructure acts as a major driver of superior profitability. The continued negative impact of LOGDESI_IDT suggests that while digital transformation is beneficial in the long run, it may still impose short-term costs, even for leading financial institutions. Also, ATM expansion positively impacts ROE at higher quantiles. Furthermore, the strong positive effects of RD, MOMENT1, and MOMENT2 indicate that banks with higher banking performance rely more heavily on innovation, financial risk management, and advanced analytics to sustain their competitive advantage.

Furthermore, in the context of quantile regression, the location parameter represents the central tendency of the dependent variable (in this case, LOGROE) at a given quantile, while the scale parameter reflects the dispersion or variability of the regression residuals. Thus, from the quantile regression results we can also analyze the location and the scale of the dependent variable (

Table 11. Location and scale for each tau.

Tau	Location (Quantile Dependent Var)	Scale (S.D. Dependent Var/Sparsity)
0.25	1.704748	0.587206/0.966466
0.50	2.104134	0.587206/0.435862
0.75	2.302585	0.587206/0.403681
0.90	2.541602	0.587206/0.402628

Source: own processing.

). For instance, for τ = 0.25, the location and scale parameters can be interpreted as follows:

• Location (Central Tendency at τ = 0.25): The quantile dependent variable value (1.704748) represents the estimated conditional quantile of LOGROE at τ = 0.25. This indicates the level of the dependent variable at this quantile. The intercept coefficient (C = −1.470964) also contributes to the location estimation;
• Scale (Dispersion): Sparsity (0.966466) represents the variability in residuals, which provides an estimate of the spread of the error distribution at this quantile. S.E. of regression (0.393935) is another measure of the dispersion of residuals, showing the standard error of the fitted values. The standard errors of the coefficients (e.g., LOGGDP: 0.153439, LOGEBANK: 0.206130, etc.) indicate how much the estimates of the coefficients vary, reflecting the scale of variability in predictor effects.

Therefore, the location in the quantile regression context represents the central tendency or typical value of the dependent variable (LOGROE) at each quantile. Typically, the quantile dependent variable (e.g., 1.704748 for tau = 0.25, 2.104134 for tau = 0.50, etc.) is considered the location parameter because it indicates the estimated median or another conditional quantile of the distribution.

The scale reflects the variability or dispersion of the dependent variable across different quantiles. This is often associated with the sparsity measure (which helps adjust standard errors) and the standard deviation of the dependent variable (S.D. dependent var).

In Table 11, the location values are taken from the quantile dependent variable for each tau, and the scale values are indicated using the S.D. dependent var and sparsity. The sparsity measure adjusts for heteroskedasticity in quantile regression.

Moreover, the Ramsey RESET test results (

Table 12. Ramsey RESET tests, quantile slope equality test and symmetric quantiles test.

Test		Chi-Sq. Stat.	Chi-Sq. d.f.	p-Value
Ramsey RESET Test (QLR L-statistic)	(Tau = 0.25)	7.933586	1	0.0049
	(Tau = 0.50)	20.25475	1	0.0000
	(Tau = 0.75)	8.617398	1	0.0033
	(Tau = 0.90)	1.240242	1	0.2654
Quantile Slope Equality Test (Wald Test)	(Tau = 0.25)	77.26342	20	0.0000
	(Tau = 0.50)	77.26342	20	0.0000
	(Tau = 0.75)	77.26342	20	0.0000
	(Tau = 0.90)	84.31933	30	0.0000
Symmetric Quantiles Test (Wald Test)	(Tau = 0.25)	29.69842	11	0.0018
	(Tau = 0.50)	29.69842	11	0.0018
	(Tau = 0.75)	29.69842	11	0.0018
	(Tau = 0.90)	105.6690	22	0.0000

Source: Author’s own calculation.

) confirm the robustness of the estimated models, indicating that the inclusion of non-linearities does not significantly alter the findings. The quantile slope equality tests and symmetric quantiles tests suggest the presence of heterogeneous effects across different levels of banking performance, reinforcing the necessity of adopting a quantile-based approach to understanding financial determinants in the EU banking sector.

In the broader EU context, these findings highlight the importance of digitalization, innovation, and human capital investment in enhancing banking performance. While digital inclusion is consistently beneficial across all quantiles, the challenges associated with implementing digital transformation strategies underscore the need for careful planning and execution. Additionally, the persistent negative effect of banking employment suggests that efficiency improvements in employment structures remain an important consideration for policymakers and financial regulators within the EU. Ultimately, the MMQR results underscore the heterogeneous nature of banking performance in the European banking sector and the significant role of digital and financial innovation in shaping long-term profitability.

The application of the MMQR (Method of Moments Quantile Regression) methodology allows for a nuanced examination of financial and technological indicators across European Union countries. By considering the varying effects of independent variables at different quantiles of the dependent variable’s distribution, MMQR captures heterogeneity that traditional regression methods may overlook. In the context of EU countries, this approach is particularly insightful given the diverse economic structures, digital inclusion levels, and banking performance metrics that characterize the region.

When analyzing return on equity (ROE) through the MMQR framework, significant differences emerge across EU member states. Countries such as Romania and Hungary demonstrate higher ROE values, particularly in upper quantiles, suggesting that banks in these economies experience stronger profitability dynamics under favorable conditions. Conversely, Germany and France, despite their economic size and stability, exhibit lower ROE values, particularly in lower quantiles, indicating potential structural constraints on profitability. This disparity may stem from differences in banking sector efficiency, capital allocation, and digital adaptation, which influence banking performance across the quantile spectrum.

Digital inclusion and technological integration emerge as key determinants of financial outcomes, with varying impacts depending on the level of digitalization. Countries such as Denmark, Finland, and Estonia, characterized by high digital inclusion and strong E-banking penetration, show more consistent and stable banking performance across quantiles. The integration of digital technology in these economies enhances banking efficiency and operational scalability, reducing cost structures and contributing to sustained profitability. In contrast, Bulgaria and Romania, with lower digital inclusion rates, exhibit greater variability in financial returns, suggesting that technological disparities exacerbate financial sector fragility and limit opportunities for growth.

Furthermore, GDP per capita, a significant macroeconomic indicator, aligns closely with ROE trends in many EU countries. Higher-income nations, including Luxembourg and Ireland, demonstrate relatively stable ROE levels across quantiles, reflecting the resilience of their financial institutions. Conversely, lower-income countries such as Bulgaria and Greece show more pronounced fluctuations, indicating that macroeconomic conditions play a fundamental role in shaping banking sector performance. This relationship underscores the importance of economic stability in moderating financial sector volatility and fostering sustainable profitability.

Employment in the banking sector and research and development (R&D) expenditure also contribute to financial outcomes. Countries with higher R&D investments, such as Sweden and Finland, exhibit more stable ROE distributions, suggesting that innovation-driven financial services enhance sectoral competitiveness. Meanwhile, banking employment trends reveal structural differences, as some economies, including France and Germany, maintain a high number of employees despite digitalization, potentially affecting efficiency and cost structures. In contrast, Estonia and the Netherlands, with relatively lower employment figures in banking, appear to benefit from higher digitalization levels that optimize financial sector productivity.

Overall, the MMQR results illustrate that banking performance in EU countries is highly contingent on digital adoption, macroeconomic stability, and sectoral investments. While digital frontrunners such as Denmark and the Netherlands maintain robust banking performance across quantiles, economies with lower digital penetration face greater challenges in achieving consistent profitability. Consequently, policy efforts aimed at enhancing digital integration and fostering innovation are important for reducing financial sector disparities across the EU and ensuring long-term economic resilience.

The quantile process estimates depicted in Figure 3 provide a dynamic perspective on the impact of explanatory variables across different quantiles of the return on equity (ROE) distribution in European Union countries. By examining the patterns and variations across quantiles, it becomes evident that the relationships between banking performance, digital inclusion, and economic indicators differ significantly across the spectrum of ROE, reflecting the underlying heterogeneity among EU member states.

The variable GDP displays a negative influence on ROE across most quantiles, with a more pronounced effect in the lower quantiles. This suggests that countries with lower profitability levels, such as Greece and Bulgaria, are more sensitive to macroeconomic conditions, highlighting the challenges posed by weaker economic fundamentals. In contrast, higher quantiles, representing countries with more stable financial sectors such as Luxembourg and Ireland, exhibit a less pronounced dependency on GDP, likely due to the resilience and diversification of their financial institutions.

E-banking penetration, represented by LOGEBANK, shows a consistently negative impact across quantiles, albeit with varying magnitudes. The stronger effects observed in lower quantiles may indicate that in countries with less profitable financial sectors, such as Romania and Hungary, the transition toward digital banking is associated with restructuring costs or initial inefficiencies. Conversely, higher quantiles, representing digitally advanced economies like Estonia and Denmark, appear to benefit from a more mature integration of digital banking technologies, which stabilizes ROE at higher profitability levels.

Digital inclusion (LOGDIGINCL) exerts a positive and significant influence across the ROE quantiles, particularly in the middle and upper ranges. This underscores the pivotal role of digital infrastructure in supporting financial sector performance in countries with moderate to high levels of profitability, such as Finland and the Netherlands. For lower quantiles, however, the relatively weaker impact suggests that digital inclusion alone may not suffice to overcome structural inefficiencies, emphasizing the need for complementary policy measures.

The impact of technological innovation, captured by R&D expenditures (RD), is uniformly positive and intensifies at higher quantiles. This trend highlights the significant role of innovation in driving profitability in technologically advanced countries, such as Sweden and Germany, where investments in research bolster competitiveness and operational efficiency. In contrast, lower quantiles, reflecting less innovative economies, demonstrate a less pronounced benefit, signaling a lag in translating R&D investments into financial returns.

The negative association between the number of banking employees (LOGBANKEMPL) and ROE is evident across all quantiles but particularly sharp in the middle range. This pattern suggests that economies with moderate financial sector performance, such as Spain and Italy, may face inefficiencies linked to overemployment in the banking sector. In higher quantiles, digitalized countries like Estonia mitigate these effects through leaner and more efficient banking structures, further affirming the role of technology in enhancing sectoral efficiency.

The variable ATM density (LOGATM) exhibits a positive yet modest impact across quantiles, with stronger effects in the middle and upper ranges. This reflects the continued relevance of physical banking infrastructure in supporting banking performance, particularly in countries where digital adoption is widespread but physical access remains an essential component of financial inclusion.

Moment-based indicators (MOMENT1 and MOMENT2) reveal a strong and positive impact across the quantile distribution, highlighting the role of stability and variance in shaping ROE. The consistent patterns suggest that countries with lower volatility and higher predictability in financial outcomes, such as France and Germany, benefit from enhanced investor confidence and financial sector resilience.

Overall, the quantile process estimates underscore the intricate interplay between macroeconomic conditions, digital inclusion, technological innovation, and financial sector performance in EU countries. The results highlight significant heterogeneity, with digitally advanced and innovation-driven economies demonstrating more stable and robust banking performance, while structurally constrained or less digitally integrated countries face persistent challenges in optimizing profitability. These findings underscore the importance of tailored policy interventions to address structural disparities and enhance financial sector resilience across the EU.

To enhance the analytical depth of our cross-country assessment, we have addressed the variation in regulatory regimes, supervisory intensity, and digital readiness across European Union member states. These institutional differences constitute critical, yet often underappreciated, determinants of how banking automation unfolds across national contexts.

First, regulatory regimes vary significantly in terms of stringency, innovation support, and openness to fintech integration. For example, countries such as the Netherlands and Estonia have pioneered regulatory sandboxes and open banking frameworks, facilitating rapid adoption of AI and automation tools [126,127]. Conversely, other jurisdictions may exhibit more conservative regulatory postures, which delay the deployment of novel technologies due to procedural inertia or risk aversion [128].

Second, supervisory intensity—defined by the frequency, scope, and intrusiveness of oversight—shapes how banks allocate resources toward automation. High-intensity supervisory environments, such as those in Germany or France, often impose rigorous compliance protocols that require advanced IT systems and real-time data reporting. This dynamic can incentivize automation but may also elevate short-term costs [129].

Third, digital readiness encompasses infrastructure quality, digital literacy levels, and national investment in broadband and cloud technologies. For instance, Scandinavian countries exhibit strong alignment between public digital policy and private sector innovation, enabling banks to implement high-performance solutions such as predictive AI, algorithmic credit scoring, and fully digitized customer onboarding [130,131]. In contrast, countries with lower DESI scores face bottlenecks in infrastructure and skills that hinder the effective use of advanced technologies.

By incorporating this institutional heterogeneity into our analytical framework, we move beyond a one-size-fits-all interpretation of automation’s effects. Instead, we contextualize our findings within the broader political, technological, and supervisory ecosystems that mediate the relationship between digital transformation and banking performance.

Furthermore, we have expanded our discussion to highlight the short-term structural costs associated with digital transformation. These include significant upfront investments in IT infrastructure, cybersecurity measures, workforce reskilling, and compliance systems necessary to ensure regulatory alignment and operational continuity during the transition phase [132,133]. For example, digital migration often incurs temporary productivity slowdowns due to the parallel operation of legacy systems and new platforms. Additionally, initial returns on digital investment may be negative or negligible until systems are optimized, and workforce adaptation is complete. These transition costs are particularly burdensome for small and mid-sized banks that lack economies of scale.

Our MMQR results confirm that these structural costs disproportionately impact banks in the lower ROE quantiles, offering an empirical explanation for the initial adverse effects of digital integration. Nevertheless, long-term productivity and efficiency gains offset these costs, especially for institutions with robust digital governance frameworks [133].

Moreover, we have strengthened our analysis on R&D spending by providing case-based illustrations, focusing on banks such as ING and BBVA. Thus, ING’s ‘Think Forward’ digital strategy showcases how a fully integrated data-driven architecture can reduce operational costs and personalize customer experiences, leading to higher profitability [134]. Similarly, BBVA’s pioneering use of AI for credit risk modeling and customer segmentation reflects how sustained investment in innovation infrastructure translates into improved asset quality and net interest margins [135,136]. These cases substantiate our hypothesis that research and development contribute positively to banking performance, particularly for high-performing institutions capable of scaling innovations efficiently. By grounding our econometric findings in real-world strategies, we offer both theoretical and practical insights into how R&D operationalizes digital value creation.

Furthermore, the implications of cointegration have also been clarified. While we confirm a long-term relationship among variables, we acknowledge that cointegration does not prove causality. This is why we have incorporated Granger causality analysis. The Granger causality analysis (

Table 13. (a) Significant Granger causality links. (b) Banking infrastructure drivers. (c) Labor and innovation dynamics. (d) Digital ecosystem interdependencies. (e) Macro-innovation feedback loops.

(a)
Causal Relationship	Direction	F-stat	p-Value	Significance
LOGGDP ⇒ LOGROE	Unidirectional	8.063	0.0053	*** 1%
LOGROE ⇒ RD	Unidirectional	4.605	0.0340	** 5%
MOMENT1 ⇒ LOGROE	Unidirectional	7.779	0.0062	*** 1%
MOMENT2 ⇒ LOGROE	Unidirectional	5.332	0.0228	** 5%
LOGROE ⇒ LOGBANKEMPL	Unidirectional	2.751	0.0999	* 10%
LOGROE ⇒ LOGDESI_IDT	Unidirectional	5.937	0.0163	** 5%
(b)
Causal Relationship	Direction	F-stat	p-Value	Significance
LOGDESI_HC ⇒ LOGATM	Unidirectional	4.810	0.0301	** 5%
LOGDESI_IDT ⇒ LOGATM	Unidirectional	8.072	0.0052	*** 1%
LOGDIGINCL ⇒ LOGATM	Unidirectional	6.823	0.0101	** 5%
LOGGDP ⇒ LOGATM	Unidirectional	15.438	0.0001	*** 1%
RD ⇒ LOGATM	Unidirectional	3.161	0.0778	* 10%
LOGATM ⇒ MOMENT2	Unidirectional	3.136	0.0793	* 10%
(c)
	Direction	F-stat	p-Value	Significance
LOGDESI_HC ⇒ LOGBANKEMPL	Unidirectional	4.100	0.0449	** 5%
LOGDESI_IDT ⇒ LOGBANKEMPL	Unidirectional	5.434	0.0213	** 5%
LOGBANKEMPL ⇒ LOGEBANK	Unidirectional	5.857	0.0169	** 5%
LOGGDP ⇔ LOGBANKEMPL	Bidirectional	5.409/4.192	0.0216/0.0426	** 5%
RD ⇒ LOGBANKEMPL	Unidirectional	4.448	0.0368	** 5%
(d)
Causal Relationship	Direction	F-stat	p-Value	Significance
LOGDESI_IDT ⇔ LOGDESI_HC	Bidirectional	8.659/4.419	0.0038/0.0374	***/**
LOGEBANK ⇒ LOGDESI_HC	Unidirectional	3.893	0.0506	** 5%
LOGEBANK ⇒ LOGDESI_IDT	Unidirectional	7.567	0.0068	*** 1%
LOGEBANK ⇒ LOGGDP	Unidirectional	3.125	0.0794	* 10%
(e)
Causal Relationship	Direction	F-stat	p-Value	Significance
LOGGDP ⇔ RD	Bidirectional	5.443/3.703	0.0212/0.0565	**/*
MOMENT1 ⇒ LOGGDP	Unidirectional	4.802	0.0305	** 5%
MOMENT2 ⇒ LOGGDP	Unidirectional	2.973	0.0874	* 10%
MOMENT2 ⇔ MOMENT1	Bidirectional	5.266/2.969	0.0236/0.0877	**/*

Source: Author’s own calculation. Significance levels: *** 1%, ** 5%, * 10%.

a–e and Figure 4) reveals a web of significant predictive relationships across macroeconomic, digital, and institutional banking indicators. By applying a 1-lag structure over the 2017–2022 sample period, the test identifies several causality directions that inform the dynamics of bank performance, technological diffusion, and economic behavior.

The results indicate that macroeconomic growth, as measured by GDP, precedes and significantly predicts return on equity (ROE), suggesting a fundamental influence of broader economic performance on bank profitability. Moreover, ROE itself emerges as a strong antecedent of R&D investment and digital integration (DESI_IDT), implying that more profitable institutions may channel resources into innovation and digital transformation initiatives. This reinforces the view that successful banking institutions become engines of innovation through endogenous reinvestment mechanisms.

Furthermore, the role of moment-based variables (MOMENT1 and MOMENT2) is underscored by their significant Granger causality toward both ROE and GDP. Their predictive strength validates their use as instruments in quantile regression frameworks addressing endogeneity concerns and enhancing robustness. These findings also reveal the underlying complexity of the feedback loops between modelled performance and the instruments used to estimate them.

Digital infrastructure indicators demonstrate predictive relationships with the physical architecture of banking, notably the density of ATMs. Variables such as DESI_HC, DESI_IDT, and digital inclusion are all found to Granger-cause ATM deployment, reflecting how technological readiness and citizen digital behavior shape the evolution of banking interfaces. The influence of GDP on ATM density further supports this conclusion by highlighting the impact of macroeconomic strength on automation investment.

The Granger analysis (Table 13a–e and Figure 4) also confirms the predictive power of human capital and technological readiness on banking labor structures. Specifically, DESI_HC and DESI_IDT Granger-cause employment levels in the sector (LOGBANKEMPL), suggesting that countries with stronger digital skills and corporate digital adoption are more likely to experience restructuring in banking labor demands. This is compounded by the influence of GDP and R&D on employment, which collectively describes an ecosystem where digital readiness, innovation spending, and economic growth co-shape labor market outcomes.

Interdependencies within the digital ecosystem are also evident. Bidirectional causality between DESI_IDT and DESI_HC confirms a mutual reinforcement mechanism where technological integration and human capital investments drive each other forward. Additionally, E-banking usage (LOGEBANK) significantly predicts both DESI_HC and DESI_IDT, implying that consumer-facing digital behaviors act as stimuli for broader institutional digitalization.

Institutional investment (RD) and innovation processes are further shown to participate in predictive loops with GDP and banking performance. R&D both Granger-causes and is caused by GDP, revealing mutual causation and reinforcing the idea that technological investments and economic output evolve in tandem. Moment-based conditions (MOMENT1 and MOMENT2) also predict GDP, reinforcing their macro-level relevance and bolstering their econometric validity.

Finally, bidirectional Granger causality between MOMENT1 and MOMENT2 supports the theoretical notion of their internal coherence and interdependence. Their interaction underscores the nuanced behavior of the statistical instruments used, which shape not only model specification but also insight generation.

Therefore, the Granger analysis substantiates a dense network of causality across institutional, technological, economic, and instrumental variables. These relationships advance understanding of how innovation and performance are co-determined in the digital banking era. The findings offer a compelling empirical foundation for future policy and strategy formulation in both financial and regulatory spheres.

Granger causality tests identify whether one time series variable provides statistically significant information for forecasting another. In this context, the results are interpreted at three levels of statistical significance: strong (1%), moderate (5%), and suggestive (10%) (Table 13a–e and Figure 4).

From the policy implications perspective, the Granger results emphasize several aspects:

• Digital infrastructure (DESI) has predictive power over physical banking tools like ATMs and employment levels in the sector—highlighting how digital capability transitions affect banking labor structures.
• Economic growth (GDP) leads to ROE and ATM density, suggesting that macroeconomic expansion catalyzes banking profitability and the deployment of banking automation.
• Banking performance (ROE) itself drives innovative inputs, notably R&D and digital integration (DESI_IDT), showing that profitable banks reinvest in tech and digital services.
• Moment conditions (Moment1 and Moment2) used in MMQR models display strong causality toward both ROE and GDP, reinforcing the validity of their instrumental use in tackling endogeneity.
• Labor dynamics show sensitivity to both digital skill levels (DESI_HC) and economic output, affirming that upskilling is crucial in an evolving digital banking landscape.
• Bidirectional causality among policy and innovation variables (e.g., GDP and RD, DESI_IDT and DESI_HC) supports the idea of feedback loops between innovation ecosystems and macro-level growth.

5. Discussion

We validated all the formulated hypotheses by estimating the coefficients of the econometric model at different quantiles of the ROE distribution. The results confirm the existence of an asymmetric effect of emerging technologies on bank performance, indicating that banks with high levels of profitability benefit more from investments in AI and automation, while low profitability institutions fail to transform these investments into a significant competitive advantage.

The H1 hypothesis is validated through the observed impact of ATM density across the quantiles of the return on equity (ROE) distribution. The MMQR results demonstrate that ATM density (as a proxy for robotic automation and classified as a mechatronic system) is positively and significantly associated with ROE primarily in the lower quantiles (τ = 0.25 and τ = 0.50). This indicates that less profitable banks benefit more from ATM implementation in terms of operational efficiency, suggesting that the marginal gains from automation are greater for institutions with initially lower performance. The diminishing or non-significant effect at higher quantiles (τ = 0.75 and τ = 0.90) reinforces the hypothesis that high-profitability institutions already operate efficiently, thereby exhibiting diminishing marginal returns from basic automation technologies such as ATMs. Hence, the data supports H1 by showing that automation contributes more substantially to profitability enhancement in lower-performing banks.

The H2 hypothesis is validated through the observed differential impact of AI-driven variables such as EBANK, DESI_IDT, and DIGINCL across the conditional distribution of ROE. The MMQR results show that these digitalization indicators exert stronger and more statistically significant effects in higher quantiles (τ = 0.75 and τ = 0.90). For instance, the coefficient for EBANK increases in both magnitude and significance as we move from τ = 0.25 to τ = 0.90, indicating that banks with higher profitability levels are better equipped to integrate AI tools such as chatbots and robo-advisors and to leverage them for enhanced financial performance. This pattern confirms the heterogeneous nature of AI’s contribution: while digital tools are broadly beneficial, their effectiveness is magnified in institutions that already have the financial and technological capacity to adopt and scale these innovations. The observed quantile-dependent behavior empirically supports H2.

The validation of H3 is reflected in the interaction between RD (research and development expenditures) and digitalization proxies such as DESI_IDT and DESI_HC. Across quantiles, RD is consistently significant and positively associated with ROE, especially in the upper quantiles. However, its isolated impact is not sufficient. It is when RD is analyzed alongside the DESI indicators that we observe a complementary effect—in countries with higher initial levels of digitalization, the returns to R&D are more pronounced. In contrast, in lower quantiles, where digital readiness is weaker, the effect of RD is either reduced or statistically insignificant. This interaction validates H3 by confirming that R&D effectiveness is contingent upon a foundational level of digital infrastructure and human capital, aligning with the theoretical expectation that innovation outcomes depend on digital maturity.

The heterogeneity in the results across quantiles suggests that banking profitability determinants differ not only across banks but also across economic and financial structures in different countries. Several key insights emerge:

• Economic Growth and ROE: The negative effect of GDP on ROE, particularly at lower quantiles, suggests that in developing economies, macroeconomic expansion does not proportionally benefit all firms, possibly due to capital misallocation, market inefficiencies, or policy-induced distortions. In contrast, developed economies with more mature financial markets may witness a more direct positive impact of GDP on banking profitability;
• Banking Digitalization Influence: The negative impact of banking sector digitalization through internet banking use (LOGEBANK) is more pronounced in lower quantiles, implying that in less developed financial systems, smaller or struggling banks may face greater barriers to accessing credit, while well-performing banks may already have alternative financing options. In contrast, in countries with highly liquid financial markets, banking digitalization may not significantly hinder profitability;
• Digital Inclusion and Profitability: The stronger effect of digital inclusion at higher quantiles highlights that banks in more technologically advanced economies extract greater benefits from digital transformation. In countries with limited digital penetration, banks may not yet fully capitalize on technology to drive profitability;
• Human Capital Development: The positive effect of human capital development at lower quantiles suggests that in emerging economies, education and workforce skills play an important role in helping less profitable banks improve their performance. In advanced economies, where human capital is already well-developed, its marginal impact on banking profitability may be less pronounced;
• Innovation Investment: The persistently negative impact of innovation expenditures across all quantiles points to a challenge in converting technological investments into immediate profitability, particularly in countries where R&D ecosystems are underdeveloped. Advanced economies with strong intellectual property rights and innovation commercialization mechanisms may mitigate these negative effects more effectively;
• Policy and Structural Differences: The findings suggest that developing countries may need stronger institutional support to enhance the effectiveness of GDP growth, financial sector policies, and digital transformation in fostering banking profitability. Meanwhile, banks in developed countries are likely to benefit more from digitalization and research-driven innovation strategies.

The MMQR model results provide strong empirical evidence that bank profitability is shaped by a combination of macroeconomic, financial, technological, and structural determinants, with varying effects across profitability levels. The robustness tests confirm the validity of the estimated relationships, reinforcing the need for differentiated policy interventions. While digital transformation and human capital investments emerge as significant drivers of banking performance, financial sector constraints and inefficiencies in innovation investment persist as barriers to growth. Cross-country comparisons further highlight the importance of tailoring economic policies to national institutional and market conditions, ensuring that banks across the profitability spectrum can optimize their performance in line with broader macroeconomic objectives.

The analysis of the MMQR results in the context of EU countries reveals significant insights into the determinants of banking sector profitability, measured through return on equity (ROE), across different quantiles. The findings indicate that GDP levels exert a predominantly negative influence on ROE, particularly at lower quantiles, suggesting that economic expansion does not necessarily translate into higher bank profitability, potentially due to increased competition or regulatory constraints. Similarly, the size of the banking sector, as measured by the number of banking institutions, appears to diminish profitability, an effect that becomes more pronounced at higher quantiles. This pattern may reflect the challenges associated with oversaturation in the financial sector, where competition erodes margins and leads to inefficiencies.

In contrast, digital inclusion emerges as a robust driver of profitability, exerting a stronger impact on banks operating at higher quantiles. This relationship underscores the advantages of digital financial services, which enhance operational efficiency and broaden customer outreach. However, the role of government-led digitalization initiatives presents a more complex picture, as increased digital public services seem to correlate negatively with ROE. This outcome may be attributed to the disruptive effect of digital government services, which create alternative financial platforms that compete with traditional banking institutions. At the same time, human capital in digitalization demonstrates a consistent positive effect across all quantiles, highlighting the importance of a skilled workforce in navigating technological transformation within the banking sector.

The relationship between employment in banking and profitability follows a negative trajectory, reinforcing the argument that larger workforces may contribute to higher operational costs without necessarily improving efficiency. Meanwhile, ATM availability appears to have a negligible influence, with a marginally positive effect observed only in the higher quantiles, suggesting that reliance on physical banking infrastructure has diminished in its relevance to banking performance. In contrast, research and development expenditure plays a pivotal role in enhancing profitability, with a pronounced impact in higher quantiles. This result reflects the increasing importance of innovation in financial services, where investment in technology and process improvement fosters competitive advantages.

In the context of EU countries, these relationships manifest differently depending on the level of economic development, digital integration, and financial sector maturity. Countries such as the Netherlands, Sweden, Denmark, and Finland, characterized by advanced digital infrastructures and highly skilled workforces, experience the most significant benefits from digital inclusion and human capital development. The strong positive association between digitalization and profitability in these economies underscores the role of technological adaptation in sustaining competitive banking sectors. Moreover, these countries exhibit higher R&D spending, reinforcing the link between innovation-driven financial services and enhanced banking performance.

By contrast, economies with larger banking sectors but relatively lower levels of digital transformation, such as Germany, France, and Italy, encounter different dynamics. The negative impact of banking sector size on profitability is more pronounced in these countries, where traditional banking models still dominate. Regulatory pressures and the costs associated with transitioning to digital banking may contribute to this trend, limiting the potential gains from technological advancements. At the same time, the relatively weaker effect of digital inclusion on ROE in these economies suggests that the benefits of financial digitalization have yet to be fully realized, possibly due to legacy systems and structural inertia.

A distinct pattern emerges among Southern and Eastern European countries, where lower levels of digital inclusion and human capital development coincide with weaker profitability outcomes. Countries such as Spain, Portugal, Greece, and Hungary illustrate the challenges associated with digital transformation in banking, as evidenced by the lower significance of digital indicators in their banking performance. The impact of government digitalization policies further complicates this relationship, as increased public digital services may divert financial activity away from traditional banking institutions. These findings suggest that while digitalization presents opportunities for profitability enhancement, its effectiveness is contingent upon complementary investments in human capital and technological infrastructure.

Comparing our study’s findings with the existing literature can be challenging since, to our knowledge, we are the first to examine the interplay between automation technologies and banking performance using MMQR. Prior studies, such as that of Pant and Agarwal [33], highlight the increasing integration of robotics in financial services, emphasizing how automation enhances efficiency and reduces operational costs. Their work aligns with the present study’s findings that mechatronic systems, particularly ATMs, significantly contribute to operational efficiency in lower-performing banks by reducing overhead costs and increasing accessibility. Similarly, Aguirre and Rodriguez [36] underscore the role of robotic process automation (RPA) in optimizing repetitive banking tasks, a perspective that complements the present study’s emphasis on mechatronic components as fundamental drivers of baseline performance improvements.

However, the present study diverges from prior research by incorporating a distributional perspective, illustrating that AI-powered solutions exert a more pronounced influence on higher quantiles of banking performance. Tatikonda et al. [34] explore the role of AI-driven RPA in personalizing banking services and enhancing customer experiences, with findings that resonate with the present study’s conclusion that high-performing banks benefit more from AI-powered automation. This perspective is further supported by Ruiz et al. [40], who examine the hybridization of human expertise and automation, arguing that AI complements human decision-making processes rather than replacing them entirely. The current research aligns with this notion by demonstrating that AI-based banking solutions do not merely automate customer interactions but also enhance strategic decision-making, particularly in banks with already strong banking performance.

Fernandez and Aman [35] explore the impact of automation on global accounting services, concluding that automation primarily benefits financial institutions with well-established digital infrastructures. This finding is consistent with the present study’s results, which indicate that AI-driven banking automation yields greater advantages for institutions in higher quantiles of return on equity. Additionally, Lewicki et al. [38] raise concerns about job displacement due to automation in banking, while Mamede et al. [39] propose a lean approach to integrating RPA to mitigate such risks. Unlike these studies, the present research does not focus on labor market implications but rather on the differential impact of automation on banking performance across banking institutions. However, our results are similar to the body of scholarly work on banking automation and digital transformation [2,47,53].

With regard to the relationship between government digital services and banking performance, we clarify that the observed negative association in lower ROE quantiles does not suggest direct substitution but rather reflects displacement effects and integration lag. Public platforms offering tax payments, identity verification, and e-government portals have increasingly encroached on traditional banking channels, especially in economies with advanced digital infrastructure [108]. Banks that fail to seamlessly integrate with these public systems face a competitive disadvantage and diminished customer touchpoints [111,114]. In response, we have elaborated on the learning effects and technology diffusion pathways [51], suggesting that banks that invest in adaptive IT strategies and modular infrastructure are more likely to benefit from complementary innovations.

The decline in ROE associated with sector expansion likely reflects competitive saturation in mature EU markets where diminishing marginal returns coexist with incomplete cost rationalization. However, in high-performing banks, our results hint at network effects and economies of scale, consistent with the findings of Königstorfer & Thalmann [75] and Herrmann & Masawi [90], where digital scaling allows for profitability despite market pressure.

Furthermore, we have expanded our treatment of regulatory heterogeneity and its role in banking performance. Drawing from Leitner et al. [47] and the ECB Report [128], we illustrate how supervisory intensity, compliance costs, and regulatory fragmentation across EU jurisdictions influence the adaptive capacity of banks for digital transformation. Banks operating in high-regulation environments may experience slower innovation diffusion, whereas more agile regulatory regimes (e.g., Estonia or the Netherlands) foster technological adoption and fintech integration.

Moreover, Wisskirchen et al. [2] examine the broader implications of AI and robotics in financial services, emphasizing the regulatory and ethical challenges associated with automation. While this dimension is not the primary focus of the present study, the findings indirectly support the need for regulatory frameworks that ensure equitable benefits from banking automation across institutions of varying financial strengths. In this context, Leshob et al. [37] propose a structured process analysis approach for adopting RPA, emphasizing the necessity for strategic planning in automation deployment, a perspective that aligns with this study’s recommendation for targeted policy interventions.

The present study further extends the literature by employing MMQR to illustrate the heterogeneous effects of banking automation. Unlike previous research, which largely focuses on aggregate effects, this study provides a quantile-specific perspective, revealing that the benefits of automation are not uniformly distributed across financial institutions. By integrating mechatronic and AI-driven components within a unified analytical framework, the research offers a comprehensive understanding of how different forms of automation contribute to banking performance, thereby bridging the gap between studies that focus exclusively on either hardware-based or AI-driven banking automation.

This analysis underscores the need for a balanced automation strategy that aligns with institutional capabilities and market positioning. While prior studies emphasize either cost reduction or service enhancement, our findings suggest that automation’s effectiveness is contingent on an institution’s financial standing. Consequently, policymakers and banking strategists must consider the nuanced impact of automation technologies across varying levels of banking performance to ensure that digital transformation efforts yield optimal outcomes.

6. Conclusions, Recommendations, Future Directions, and Limitations

6.1. Conclusions and Novelty of the Study

This study highlights the heterogeneous effects of economic, financial, and institutional factors on banking performance across the EU. While economic growth and efficiency appear to benefit top-performing banks more than their lower-performing counterparts, digital inclusion, innovation, and human capital investments emerge as key drivers of ROE across all quantiles. The results emphasize the importance of differentiated policy interventions to bridge the performance gap between EU economies, ensuring that banks in all member states can leverage financial and technological advancements to enhance profitability.

This study brings significant contributions to the field by employing Method of Moments Quantile Regression (MMQR) to analyze the impact of automation technologies on banking performance. Unlike traditional approaches that focus on average effects, MMQR captures the heterogeneity of impact across different levels of banking performance, revealing nuanced relationships that remain obscured in conventional regression models. The novelty lies in the simultaneous examination of hardware-based technologies, represented by ATMs as mechatronic systems, and software-driven solutions powered by artificial intelligence, such as chatbots and robo-advisors. This dual perspective offers a comprehensive understanding of how these complementary technologies influence banking performance, particularly in a rapidly digitizing financial environment where physical banking infrastructure is gradually being phased out.

Furthermore, this study breaks new ground by integrating digitalization metrics, such as AI-driven customer interaction tools and banking automation, with traditional indicators like research and development expenditures, GDP, and human resource allocations. The contextualization of these variables within a European Union framework provides a unique lens to explore the disparities in adoption and effectiveness of automation across a diverse economic and regulatory landscape. The findings contribute to bridging an important gap in understanding the role of automation in the banking sector as it transitions toward a fully digital future.

The findings derived from the MMQR analysis underscore the heterogeneous impact of economic and digitalization indicators on banking profitability across EU countries. The relationship between digital integration, the structural composition of the financial sector, and the regulatory landscape significantly influences the extent to which banks capitalize on technological advancements to enhance banking performance. Economies with a high degree of digitalization benefit substantially from innovation and a highly skilled workforce, whereas those lagging in digital transformation encounter structural limitations that constrain their profitability. These results emphasize the necessity of targeted policy measures aimed at creating an environment conducive to digital banking, ensuring that financial institutions can effectively navigate and adapt to the evolving economic and technological landscape.

6.2. Policy Recommendations

As the banking sector transitions toward a fully digital model, policymakers face the urgent challenge of ensuring that this transformation is both inclusive and sustainable. The gradual phasing out of ATMs, combined with the increasing reliance on digital tools, calls for strategic investments in digital infrastructure to bridge the accessibility gap. Policymakers must prioritize the development of high-speed internet connectivity, particularly in rural and underserved areas, to guarantee equal access to digital banking services. Additionally, efforts to enhance digital financial literacy are important for empowering individuals, especially those from vulnerable populations, to adapt to a banking ecosystem dominated by technology.

Regulatory frameworks must also evolve to address the growing reliance on AI-based tools, such as chatbots and robo-advisors. Policymakers should enforce transparency standards for algorithmic decision-making to prevent biases and ensure equitable access to services. Cybersecurity is another significant area, requiring robust regulations to protect customer data and mitigate risks associated with digital financial systems. Beyond safeguarding user trust, these measures can foster innovation by creating a secure environment for the adoption of advanced technologies.

The elimination of physical banking infrastructure, such as ATMs, raises concerns about financial exclusion for certain demographics. Policymakers should explore alternative mechanisms to maintain access to essential banking services, such as mobile banking units or community financial hubs, particularly in regions where digital adoption remains low. Supporting the transition through fiscal incentives for banks investing in automation, as well as subsidies for fintech collaborations, can accelerate the adoption of innovative solutions while minimizing disruptions.

This study highlights the importance of interoperability in digital financial systems, urging policymakers to promote collaboration among banks, fintech companies, and public institutions. Such cooperation can ensure the seamless integration of automation technologies across platforms, enhancing efficiency while maintaining fair competition. Additionally, regular monitoring of the social and economic impacts of digital transformation is essential for adaptive policy making, enabling governments to respond effectively to emerging challenges and opportunities in the financial ecosystem.

6.3. Future Research Directions

Future research directions can extend the analysis by exploring additional factors, such as the degree of regulation of the banking sector in each country or the level of cybersecurity of digital financial infrastructure. In addition, the use of more sophisticated econometric methods, such as machine learning, to predict the impact of digitalization on banking performance, could improve the accuracy of the results and allow the identification of emerging trends not captured by traditional approaches.

6.4. Limitations of the Study

In terms of the limitations of the study, the main constraint is data availability, as banks’ direct investments in AI solutions are not systematically reported at the European level, which requires the use of proxy indicators. Also, the proposed model does not consider consumer behavioral factors, which can significantly influence the adoption of digital technologies in banking.

In conclusion, this study contributes to the literature by providing an innovative perspective on the impact of automation and AI on banking performance, highlighting that the effects of these technologies are not uniform and that digitalization policies need to be tailored to the specifics of each banking system. The results provide a valuable starting point for regulatory strategies, suggesting the need for targeted investments in digital infrastructure and in the technological skills of financial human resources.