Emerging Risks in the Fintech-Driven Digital Banking Environment: A Bibliometric Review of China and India

Abstract

The digital revolution is transforming the financial services sector. Risk is not static; emerging risks continue to pose threats to the financial services sector which influences financial stability and consumer protection regulation mandates. This novel study presents a comparative bibliometric analysis of China and India in examining the effect of trends on the scholarly research outputs discussing the emerging risks in the fintech-driven digital banking environment. Furthermore, the mapping presents the geographical dynamics of Asia, followed by country-level perspectives. The period of study was from 2015 to 2024. Leveraging the Scopus database, data was extracted based on a specified query using the SPAR 4 SLR protocol. Analysis was performed on 162 articles from an initial list of 1257 articles using Scival and Vos viewer tools. Performance indicator metrics and science mapping enabled the answering of research questions. The findings revealed that research output is inclined towards India rather than China; this is despite China domiciling some big tech firms. Comparatively, India dominates when it comes to performance analysis metrics compared to China. The scientific mapping depicted in both countries shows the multifaceted effects of fintech on banking, including trends in user acceptance, competition, emerging risks, technological innovation, and financial stability. The strong connections in both countries across clusters highlight how fintech research is multi-disciplinary, spanning consumer behavior, finance, economics, and financial technology. This study provides a foundation on which a robust risk management framework, which is customized to digital banking existence, can be developed in the face of emerging risks.

1. Introduction

The incorporation of financial technologies within the banking sector has resulted in the transformation of the sector and created a win-win situation for banks and customers. The transformation includes 24-h banking services access, self-serving machines, and financial integration, among others, which advance digital banking. Additionally, the digital age has led to a paradigm shift in business models. With digitalization increasing, it is becoming integral in customers’ lifestyles as it brings convenience in accessing banking services. Digital banking has become increasingly crucial as new risks and vulnerabilities emerge in the digital landscape.

Historically, traditional financial institutions have long been at the forefront of using information technologies (Windasari et al. 2022; Haddad and Hornuf 2019). However, today’s world is defined by rapid technologically induced innovative solutions that allow for an open environment. Dissanayake et al. (2023) observed that the financial services sector is experiencing a significant shift, marked by the appearance of innovative financial technological solutions which pose a threat to conventional banking services. For instance, new ways of interacting with customers have emerged because of the integration of both the digital and physical environments (Tanda and Schena 2019).

Aljudaibi and Amuda (2024) concurred with Dissanayake et al. (2023) that there has been a noticeable rise in the frequency of customer engagement across various interaction channels during the last decade. Above all, financial institutions have been forced to adapt to this shift by introducing hybrid customer engagements which are physical and virtual. For this reason, technological innovations coupled with evolving modern consumer expectations lay the path for a more digitized future. Understandably, physical branches and staff are being rendered redundant as banking services and delivery channels undergo a massive transition from traditional, non-digitalized operations backed by bank employees to completely digital and self-service. Despite fintech taking some market share from the banks, it exists not to substitute banks, but to complement the existing banking service provision (Elsaid 2023; Bunea et al. 2016). Gaviyau and Sibindi (2023) posit that the foundation for banking is now digital, but real innovation is the ultimate test.

In terms of risks, traditional banking regulations fail to keep up with the rapid emergence of digital financial services since they were developed for brick-and-mortar types of institutions. On the other hand, both digital and traditional banks face similar risks. Thus, the transition to digital banking requires responsive regulations. Indeed, digital banking has become part of many people’s daily lives, and this business model is relatively new.

In designing the digital banking regulations as part of the financial services sector policy, there is a need to be cognizant of the dual policy challenges of seeking to ensure the financial system’s safety and to protect customers’ interests. Furthermore, another requirement is to leverage the emerging opportunities and tackle associated challenges in the new digital landscape. Undoubtedly, the banking sector must concurrently address the associated emerging risks and implications of protecting client data and privacy (Duan 2024; Allen et al. 2021).

A previous study by Tuli (2023) focused on the factors inhibiting the adoption of digital banking within the Asian continent by using a structured literature review and bibliometric analysis. The study by Tuli (2023) was location-specific, which can make generalizing the findings to other locations difficult. Another study by Aziz et al. (2021) analyzed the literature on digital banking and financial inclusion by applying bibliometric analysis for the 2014–2020 period. The study highlighted the emerging patterns and trends associated with digital banking and financial inclusion. They recommended that researchers should keep abreast of the latest developments for the betterment of the banking and finance industry as their study was limited to the 2014 to 2020 period.

Jain et al. (2021) conducted a study on the systematic literature review of the risk landscape in the fintech industry. The study revealed the existence of asymmetric information between regulators, supervisors, and financial markets. They recommended the enactment of legislation on fintech. Another study by Acosta-Prado et al. (2024) examined the literature trends of digital banking adoption in emerging economies through bibliometric analysis and revealed increasing concerns on cybersecurity risks and user data protections.

Accordingly, the aim of the study is to examine trends of scholarly research outputs on the emerging risks in the fintech-driven digital banking environment, with a specific focus on the Asian countries India and China. This study attempts to answer the following research questions: (1) What is the status of the performance-related scholarly output indicators on emerging risks in the fintech era? (2) What is the scientific mapping status of the existing knowledge landscape within emerging risks in the fintech era? By narrowing the focus of the study onto China and India, the methodological approach allows delivery of insights which offer a deeper understanding of emerging trends and developments. This novel study focuses on comparative bibliometric mapping of fintech-related risks existing within digital banking in India and China. Furthermore, the mapping brings both the geographical dynamics of Asia and country-level perspectives. Thus, the study complements prior studies which did not bring the comparative geographical dynamics into perspective.

China and India have been classified by the International Monetary Fund (IMF) as the emerging and developing economies in Asia (IMF 2025). India and China, as the emerging economies within Asia, have been at the forefront of the financial technologies revolution by having policies which encourage development of innovative technologies to spur economic growth (Deepak 2025; Jacopin 2021). Notably, expansion of the digital revolution is reshaping Asia and propelling the economies to sustainable economic growth with various developments (Simwanza 2025). For instance, digital companies such as Alibaba, Baidu, and Tencent offer services ranging from cloud computing, ecommerce, and fintech. Most of the countries in Asia have developed policies to promote the digital economy, digital infrastructure, digital inclusivity, and digital integration. Resultantly, digital technologies have created opportunities and new avenues for innovation and productivity.

Bibliometric studies are very significant for identifying existing knowledge gaps on specific aspects such as countries, theories, or methodologies (Garg et al. 2023). In this case, the study provides useful information to guide current and future studies in digital banking and risk management. Risk is not static, and emerging risks continue to pose threats to the financial services sector which influences regulation mandates such as financial stability and consumer protection. Thus, the study is significant to others in the academic community, researchers, fintech players, financial institutions, and financial regulators as it helps them to be aware of emerging risks given that fintech is transforming the banking sector. Additionally, resources can be directed towards researching and proffering solutions given the critical nature of the banking sector to the economies.

Against this background, the paper is organized into six sections. Section 1 focuses on the introduction to the subject under study, which is followed by Section 2 which discusses the literature underpinning the study. Thirdly, presentation and discussion of results are revealed in Section 3. Fourthly, Section 4 focuses on the research methodology adopted. Fifthly, Section 5 provides the conclusions and directions of future studies. Finally, Section 6 highlights the limitations of the study.

2. Literature Review

To enhance understanding of the emerging risks associated with fintech-driven digital banking, the literature was gathered. The literature focuses on the theoretical foundation, emerging risks, and evolution of banking integrating with technology. Importantly, there is a need to trace the origins and how the banking sector has evolved over the years, with technology being the driving force. Firstly, to be discussed is the theoretical foundation, followed by the evolution of the banking sector’s integration of technology, and lastly the emerging risks in the digital era.

2.1. Theoretical Literature

The three theories provided an insight into the foundation upon which the future of banking is anchored and responding thereof. These theories are namely, institutional theory, information asymmetry, and holistic digital maturity model. These are discussed below:

2.1.1. Institutional Theory

Institutional theory was advanced by Scott in 1987. The theory postulates that procedures and mechanisms form structures, rules, and behaviors to explain social behaviors. Additionally, this instills values as organizations are continually evolving in response to the business environment. Banking is anchored on the values of trust and confidentiality, with bank costumers expecting a continuation in the digital era where there is minimal bank–customer physical interaction.

Institutions are at different levels of institutionalization as they are not constant (Peters 2000). Scott (2004) argued that the existence of institutions is due to social interactions among people. As social interactions take effect, this influences the powers of groups, shapes ideas, and influences policy coordination and public decision-making. Generally, organizations change over time in terms of their effectiveness.

Institutional theory is anchored on the integration of social, political, and economic factors. The theory attempts to distinguish why some organizational structures and procedures become predominant while others may be marginalized or even have an unfavorable prospect of weakening completely (Agoba et al. 2023; Hallett and Hawbaker 2021). By recognizing institutional forces, banks may better manage the competitive landscape through incorporating digital innovations to meet the evolving stakeholders’ needs and expectations.

2.1.2. Asymmetric Information Theory

Three researchers named Akerlof, Spence, and Stiglitz advanced the information asymmetry theory in 1971 (Chen 2023; Auronen 2003). Since the advancement, the theory has been used to explain and describe phenomena within the economic and business domain, among others (Chen 2023).

Asymmetry of information among economic participants is reduced by an intermediary market institution known as a counteracting institution (Nygaard and Silkoset 2023; Auronen 2003). When applied to this study, the regulator serves that role of reducing information asymmetry by regulating the financial services sector. The evolving sector has fintechs, banks, big tech firms, and service providers who target the same customers with different information sets which make them competitive. By design, regulations are meant to correct market imperfections, as failure can result in excessive risk-taking behaviors (Iyelolu et al. 2024). Therefore, the existence of a regulator aids in improving information availability.

This theory postulates that economic agents operate in an environment associated with biased and incomplete information. Information availability influences the behavior of economic participants. Decisions are based on this set of imperfect information to attain economic gain. Thus, the theory offers insights into the current operating environment, where fintechs have identified opportunities within the banking sector while the traditional banks are slowly adapting and responding because of legacy systems. On the other hand, fintechs with better ways of collating information serve as an avenue of transforming the banking sector.

Applying it to this study, with the incorporation of fintech into banking sector consumer decision-making security and trust in the digital environment can be improved. Hence, this requires regulatory responses such as disclosure requirements, regulatory licensing, and consumer protection, among others.

2.1.3. Holistic Digital Maturity Model (HDMM)

The model was advanced by Aras and Büyüközkan (2023), who developed the model based on the shortfalls of the other maturity models. The maturity models first appeared within the software engineering domain in the 1970s (Minh and Thanh 2022; Chanias and Hess 2016). Indeed, this has evolved into a crucial tool to assess the status quo and improve it. Berghaus (2016) opines that a maturity model offers guidance on the approach entities can adopt to plan and implement digital transformation. A critique of the maturity model argued that the model being centered on improving specific organization’s capabilities entails further development of the model in line with digital transformation (Minh and Thanh 2022).

The holistic digital maturity model is based on six interacting elements in the journey of digital transformation. Based on each organization’s assessment they would be able to identify existing gaps between the current situation and intended goals. The six dimensions are digital strategy, digital value, digital processes, digital technology and data, digital work, and digital governance.

Firstly, digital strategy assesses long term organizational aspects that create value in digital transformation. Secondly, digital value focuses on the value creation from the product to customer offering. This entails new product offering, innovation, and customer value offering. Thirdly, digital processes examine how the organization’s process architecture and business process management are affected by strategies and the management of the digitalization processes.

Fourthly, there are digital technologies and data which evaluate the technologies and solutions, ensuring that the transformation process in undertaken in a sustainable manner. The fifth dimension, digital work, focuses on upskilling employees in the digital transformation agenda to remain competitive. Lastly, digital governance focuses on managerial and cultural aspects that need to be addressed to ensure the successful implementation and continuation of the digital transformation.

Gökalp and Martinez (2022) argued that the inclusion of digital governance remains key to the success of the digital transformation process. Though the model is generic it can still be applied in the financial services sector to guide the transformation process to digital banks and banking. Furthermore, it provides an opportunity to develop targeted solutions to accelerate the transformation. Thus, the holistic model allows for the examination and management of all the elements within the digital transformation journey. Digital transformation should be considered as an ongoing and iterative growth process for steering company success.

2.2. Banking Evolution and Integration with Information Communication Technologies (ICT)

Since time immemorial, businesses have made use of technology in business operations and with developments changes have been implemented. These changes include modern production methods, customer engagements, and business operations, among others, which have led to spurred productivity. Importantly, the financial services sector was not spared, and information technologies have remained core to their functions.

Megargel et al. (2018) observed that technological changes have gone through three stages, namely data processing, client server, and predictive. Noticeably, the banking sector has evolved from banking 1.0 to the current banking 5.0 (Chougule and Dudekula 2024; Rathnayake 2023). Mehdiabadi et al. (2020) observed that banking 1.0 refers to the traditional banking concept which operated pre-1960, where banking services were personalized with branches operating at specified times. According to Megargel et al. (2018), due to the analog system that was being used the period was known as the data processing years. Transactions in the banking sector were primarily performed at the branches in bulk, with processing at the end of day. The period, also known as banking 2.0, advanced from banking 1.0 as banks started having off-branch operations and branch networks growing (Chougule and Dudekula 2024).

Banking 3.0 stretched from the 1980s to 2000s. During this period banks facilitated IT-based infrastructures to enable bank clients to interact with the bank in addition to the new service offered by banks. The new service offerings were prompted by innovative technologies such as internet banking, which was popularized in the 1990s with the advent of banks having worldwide websites. Subsequently, banking 4.0 encourages collaboration and innovation in the financial services ecosystem with real-time processing of transactions.

Banking 5.0 is going to be characterized by exchange-traded funds (ETFs), cryptocurrencies, high-frequency trading, intelligent banking, robo-advisors and cobots, embedded banking, and responsible banking. Thus, banking 5.0 will require greater collaboration between humans and intelligent systems (Chougule and Dudekula 2024; Rathnayake 2023).

Comparing banking 4.0 and banking 5.0, collaboration in banking 4.0 centered on financial institutions with fintechs while in banking 5.0 it is centered on humans and intelligent systems. This shows great advancement in the interaction, which requires traditional banks to move away from legacy systems.

2.3. Emerging Risks

Fintech has revolutionized the traditional financial system, resulting in improved access to financial services and innovative banking practices, with convenience underpinning this. Alongside the benefits, fintechs have brought emerging risks that must be carefully addressed in line with the regulator’s mandates, such as consumer protection and financial stability. The emerging risks include cybersecurity, operational, data protection, and ethical challenges. Discussed below are some of the risks:

2.3.1. Cybersecurity and Data Privacy Risks

Cybersecurity remains a significant issue faced by the financial services sector. Financial services are increasingly digital, making systems and bank customers more vulnerable to identity theft, data breaches, and cyberattacks (Duran and Griffin 2021). Sahu and Kumar (2024) highlighted that though decentralized financial platforms promote peer-to-peer transactions and eliminate bank charges they also contribute to increased cybersecurity risks. Furthermore, Daiya (2024) concurred with Grassi and Lanfranchi (2022) that fintech applications integrated with machine learning and artificial intelligence contribute to the mitigation of associated risks.

Related to cybersecurity is data privacy. Fintech integrates hard and soft information to customize new services offering; hence, the competition with traditional banks who relied on one set of information. Information harvesting from different sources can entail unauthorized access or potential misuse (Fischli 2024; Hua and Huang 2021; Goddard 2017). Due to weak or no data protection regulations in many countries, enactment of data protection mechanisms is required. The General Data Protection Regulation (GDPR) from the European Union can serve as a data protection framework for countries to customize. Without the legal data framework, consumer trust can be eroded and expose the sector to financial and legal penalties, among others.

In some countries, sharing personal details infringes on privacy rights (Bakare et al. 2024). The biometric identification system adopted in India, Aadhaar, was legally challenged on data privacy issues since collation of data was being handled by a third party. Biometric information on the Aadhar system was open to abuse through fraud and misuse (Sadhya and Sahu 2024; Abraham et al. 2017). In 2017, the Supreme Court of India agreed that privacy rights had been infringed but failed to rule on the constitutionality of the case brought before the courts. This shows that a country’s laws and regulations, culture, and religion can be a hindrance to uniform global implementation of data standards.

Existence of disharmony continues to present challenges to banks, regulators, and academics regarding the need to find common ground for the betterment of the financial services sector (Ofoeda et al. 2022; Heller 2017). Likewise, the advent of blockchain technology can aid in resolving these challenges. Similarly, data privacy issues serve as impediments to blockchain technology and fintech adoption (Al-Tawil 2023). Simplification of internal processes by overcoming data issues can shift regulations to data-based regulations.

2.3.2. Regulatory and Compliance Risk

Fintech’s rapid growth has resulted in regulations lagging, posing serious risk to regulatory compliance. These regulatory gaps together with limited fintech-related regulations worldwide create an uncertain environment threatening financial stability (Khazratkulov 2023; Grassi and Lanfranchi 2022; Ryu 2018). Additionally, the cross-border nature of fintech complicates harmonization of regulations. Nonetheless, this regulatory arbitrage opens opportunities for fintech, which exposes the inadequacy of the wider financial services sector to address consumer protection-related issues.

Financial institutions have been able to enhance operational efficiency, improve customer service, and reach a wider audience by integrating innovative technologies such as blockchain, artificial intelligence, and big data analytics (Obeng et al. 2024). But these developments have also presented emerging challenges for the current and traditional regulatory mandates of consumer protection and financial stability. In the same vein, fintech brought about new financial products, digital payment methods, and decentralized finance (DeFi) platforms, all of which frequently function outside of the traditional financial regulatory framework. As fintech solutions move across global and jurisdictional boundaries, regulatory compliance in areas like data protection, know your customer (KYC) regulations, and anti-money laundering (AML) have grown increasingly complex. In the digital era, the know your customer (KYC) principle is being transformed into know your data (KYD) (Velez 2024; Ismayilov and Kozarević 2023).

Akartuna et al. (2022) posits that cryptocurrencies facilitate money laundering and illicit transactions due to their anonymity and decentralized nature, necessitating the development of targeted regulatory measures. Money laundering risks are compounded by the accessibility of fintech platforms, which allow criminal actors to create false identities and obscure transactional trails, undermining efforts to combat financial crimes (Ismaeel 2024; Ajdini 2024; Nazzari 2023).

Haralayya (2024) asserts that regulatory frameworks need to change to consider fintech’s innovative nature while maintaining financial stability, market integrity, and consumer protection. Thus, there is need for a regulation paradigm shift which strikes a balance between innovation and risk management. Also, regulators should be proactive by encouraging incorporation of technology into the regulatory process, promoting innovation by testing new advances in a controlled environment through regulatory sandboxes.

2.3.3. Operational Risk

Operational risk is the possibility of suffering a loss because of weak or inadequate internal procedures, personnel, systems, or other external factors. This type of risk arises from several factors, such as human error, failed processes and systems, and natural disasters. All of these factors can lead to reputational damage and financial losses. The incorporation of fintech into the financial services sector exposes the sector to systemic risks.

Operating challenges are rendered inefficient by more dependence on modern technologies such as blockchain and artificial intelligence. This further complicates operating system integration and maintenance, especially for traditional banks with legacy systems.

Over the past 20 years, financial services have been the major user and purchaser of information technology services globally (Tsingou 2022; Arner and Barberis 2015). Saksonova and Kuzmina-Merlino (2017) argued that the legacy systems of financial institutions contribute to their failure to innovate and adopt new technologies. Due to legacy issues, fintech-driven organizations can exploit the opportunity through the provision of cost effective and efficient modern banking services. Resultantly, customers prefer the better customer experience offered by fintech-driven banks compared to traditional banks.

Traditional banks have struggled to develop robust and efficient systems to tackle compliance issues due to legacy systems (Tsingou 2022; Marous 2018). Compliance-based data requires data extraction from different sets within the banking system. Integration, algorithm-based data aggregation, and automation can only be extracted using modern technologies.

Legacy systems make compliance expensive, and they are very slow at obtaining the requested information or data (Sreekanth and Kiran 2024; Marous 2018). For example, an online customer boarding process using legacy systems costs at least USD 10 million and must be implemented over a two-year period (Lyman et al. 2019). Comparably, using modern technologies the same process costs USD 0.3 million and can be implemented over a three-month period. The FATF recommendation number 17 of money laundering requires that extreme caution be observed when integrating technology from third-party sources with banking functions (Gaviyau and Sibindi 2023; Omar and Johari 2015).

The ability to integrate risk functions is based on access to high-quality data which is consistent across the organization (Paramesha et al. 2024). In traditional banks this appears to be insurmountable due to legacy issues and compliance with internal data standards and practices. By resolving this challenge, the data quality and accuracy are improved to a level needed to support data-driven decisions in real time.

Essentially, with the current scenario of huge data, solutions include effective data management and advanced analytics. Data analytics helps banks to identify risky patterns or unusual account activity, thereby deterring any criminal intentions (Javaid 2024). By utilizing data, banks can have better insights into customer relationships. The relationships can be analyzed using massive data of current and historical transactions.

2.3.4. Ethical and Societal Risks

Digital technology has transformed the working culture in the financial services sector. Additionally, traditional financial institutions have been compelled to adapt the way they operate. By adapting to the new norms they embrace inclusion and diversity, encourage innovation and new ways of thinking, and foster transparency and higher levels of customer trust (Prastyanti et al. 2023).

To offer a sufficient degree of client protection, banks, as regulated entities, must follow responsible credit practices in line with the applicable legislative framework (Sampat et al. 2024). Unregulated entities such as fintechs engage in unethical or predatory lending practices with limited or no consumer protection mechanisms in place. The procedure used for assessing consumer credit loan applications has changed due to the extensive use of econometric techniques and machine learning systems. Automated credit application analysis reduces the subjective nature of the decision-making process. On the other hand, machine learning, which is based on historical decisions documented in financial institutions’ datasets, frequently reinforces preexisting prejudices and biases (Garcia et al. 2024). The biases are based on elements such as ethnicity, sexual orientation, and gender, among others.

Inherent biases in financial institutions’ datasets can lead to discriminatory outcomes which undermine the financial inclusion goal envisioned by fintech (Grassi and Lanfranchi 2022). To address the inherent biasness within the artificial intelligence-driven financial service sector, regular algorithm audits should be conducted along with stakeholder collaboration (Chomczyk Penedo and Trigo Kramcsák 2023).

Emphasis has focused on how critical it is to solve these ethical issues by reestablishing digital ethics in the fintech sector and guaranteeing that technological ideals like accountability, transparency, fairness, and access are realized (Aldboush and Ferdous 2023; Prastyanti et al. 2023). Prastyanti et al. (2023) argued that to build a sustainable future for fintech and digital banking a balance should be struck between technological innovation and moral behavior. Indeed, the greatest competitive advantage, which sets regulated banks from fintech entrants, is regulatory compliance (Macchiavello and Siri 2022).

3. Results and Discussion

This section answers research questions 1 and 2 by presenting the results and discussing them. Research question 1 is answered by performance analysis while research question 2 is answered under scientific mapping.

3.1. Performance Analysis

According to Donthu et al. (2021), performance analysis is a bibliometric technique that highlights the dynamics and impact of an identified subject matter, in this case, emerging risks in the fintech-driven digital banking environment. In this study, the following performance metrices were undertaken, which unveiled the following insights that answer research question 1.

3.1.1. Publication Trend

Figure 1 shows the publication trends on the subject under study. Of the 162 articles published the growing trend started in 2021, which recorded double-digit figures, with the highest of 60 recorded in 2024. The period prior to 2021 had single-digit output, with 8 articles being the highest recorded in 2019 and 2020 consecutively. This data indicates a growing academic research trend in China and India concerning this emerging topic. Among others, some of the research outputs have centered on fintech’s impact on bank performance (Xu and Qi 2025), risk management (Litimi et al. 2024), and digital payments (Laxman et al. 2025).

Another plausible explanation for the growth is the financial service sector stakeholders’ interest in how the sector evolves in terms of regulation, risk management, competition, efficient service delivery, and consumer protection. The peaks, especially after 2020, can be attributed to the COVID 19-induced restrictions of 2020 to 2021 which accelerated the shift towards digital banking.

Critically, during the COVID 19 pandemic research interest was developed to ensure continuity and efficiency in banking operations. Coupled with fintech innovations post-2007/2008 global financial crisis (GFC), solutions such as the usage of physical and digital banking platforms and incorporating biometrics in the customer–bank relationship, among others, arose (Obeng et al. 2024). These fintech solutions indicate the shift towards banking 5.0, which requires collaboration between humans and intelligent systems. However, these opportunities underscore the importance of robust risk management frameworks as emergent risks arise. Thus, banks with legacy systems face challenges in adapting to the new environment. Legacy systems have been identified as an inhibitor to banking transformation (Sreekanth and Kiran 2024; Marous 2018). In line with institutional theory in Section 2.1, banks should transform to meet stakeholders’ expectations.

3.1.2. Research Output by Country and Institutional Contributions

Addressing research question 1 requires information on the research output by the countries under study. Though the study is centered on China and India, information about the leading publications can assist in influencing research policy in these countries. Also, this can indicate that these countries are at various development stages regarding supporting the fintech-driven academic research capacity of their research institutions. Of the two countries shown in Figure 2, India accounted for 57% of the research articles and 43% were from China.

India’s growth is supported by the Indian government’s encouraging rules and regulations, with programs such as India Stack, and growing bank–fintech startup collaboration (Desai and Ramachandran 2025). The collaboration is around incubators, partnerships, and supplementary offerings. This collaborative partnership is an indication of banking 4.0. Relating to information asymmetry theory, fintechs serve as an avenue of transforming the banking sector. India Stack is an open initiative which allows government, start-ups, developers, and businesses to utilize unique digital infrastructure to solve the country’s technological problems. This initiative aligns well with banking 5.0 in utilizing application programming interfaces (APIs) and requiring ideas from stakeholders. Opening up to ideas aligns with the digital transformation journey advanced by the holistic digital maturity model (Aras and Büyüközkan 2023).

China’s government has supportive policies and big tech giants. Big tech firms, also known as tech giants, are technological companies who leverage their data analytics capabilities, vast customer base, and brand recognition to offer financial services (Armstrong et al. 2020). Offered services include payment and credit services. For instance, China has BATX—Baidu, Alibaba (Alipay), Tencent (WeChat Pay), and Xiaomi. Furthermore, BATX have a bigger presence in the domestic financial markets (Dziawgo 2021). Big tech firms gather information from their financial and non-financial operations and use customer data stored in various areas of their company like social media. Progressively, firms in China’s financial services sector have integrated BATX into their banking operations, which aligns with banking 5.0.

Bains et al. (2022) opine that to host essential IT systems such as cloud-based services, which boost security and efficiency, incumbent financial businesses have become more dependent on big tech companies. Consequently, big techs’ exponential growth in the financial services industry, together with their connections with other financial service companies, gives rise to new avenues for systemic risk.

Furthermore, unregulated entities such as big tech engage in unethical practices with limited or no consumer protection mechanisms in place. Indeed, the greatest competitive advantage, which sets regulated banks apart from fintech entrants, is regulatory compliance and the integration of innovation and risk management within the banking system (Macchiavello and Siri 2022). Hence, the integrated approach by India and China addresses some of the consumer risk issues.

In terms of institutional contributions, Amity University (India) is the leader, followed by Symbiosis International (India), Chitkara University (India), Christ University (India), and Chinese Academy of Sciences (China). Whereas these academic institutions have made significant contributions to the emerging risks associated with the digital-driven banking environment, they should collaborate with other institutions to enable the cross-sharing of research rigor and practices.

3.1.3. Publication by Discipline

The area of discipline indicates the extent of growth of the subject under discussion. Figure 3 reveals that the subject most appeared in the discipline of computer science, with 29%, followed by business, management, and accounting, with 20%, while economics, econometrics, and finance was at 15%. Notably, the subject of financial technology, digital banking, and emerging risks is related to the discipline of finance. However, with the research output mainly in the discipline of computer science this indicates the multi-disciplinary nature of the subject.

Primarily, fintech falls under the finance subject discipline, though fintech is a blend of finance, technology, and computer science due to the reliance on algorithms, software development, and digital platforms (Liang 2023; Stulz 2019). Taherdoost (2023) concurred with Das (2019) that fintechs emerged because of significant advancements in computing technology, econometrics, and mathematical and statistical disciplines. Indeed, fintech is multi-disciplinary. Multi-disciplinary research is the generative process of harvesting and leveraging on the skills and expertise of the various parties linked to the research (Duan 2024).

3.1.4. Publication by Type

Research output is published in the form of articles, conference papers, book chapters, and books, among others. Figure 4 shows that the research output by academic scholars in China and India is mainly produced in the form of articles, which accounted for 39%, followed by conference papers, with 32%, and book chapters, with 20%. This indicates that the research output by academics is being shared through articles in internationally recognized journals and acceptable conferences. Publishing research remains a powerful way to share findings, gain professional recognition, open collaboration opportunities, and contribute to the advancement of knowledge. The type of publication chosen can have a bearing on the researcher’s success in the specified discipline (Tenopir et al. 2016).

3.1.5. Most Productive Publications

CiteScore has become a widely used tool for assessing the scholarly performance of publications, with it indicating growing academic influence, citation impact, and relevancy (Channuie 2025).

Table 1. Most productive publications in India and China by CiteScore.

Publication Name	Type	Cite Score	SCImago Journal Ranking (SJR)	Source Normalized Impact per Paper (SNIP)
IEEE Transactions on Engineering Management	Journal	9.7	1.134	2.262
PLoS ONE	Journal	5.4	0.803	1.065
Journal of Intelligent and Fuzzy Systems	Journal	4.2	0.364	0.621
ACM International Conference Proceeding Series	Conference Proceeding	2.0	0.191	0.367
Lecture Notes in Networks and Systems	Book Series	1.0	0.166	0.233

shows the productive sources used by researchers in India and China of the fintech era based on CiteScore, SJR, and SNIP scores. Over the period under study, the researchers published in journals, participated in conference proceedings, and submitted book series. Of the journals, the main ones used were IEEE Transactions on Engineering Management, which had a high cite score of 9.7, followed by PLoS ONE, with a cite score of 5.4, and Journal of Intelligent and Fuzzy Systems, with a cite score of 4.2. The ACM International Conference Proceeding Series had a cite score of 2.0, and lastly the book series Lectures Notes in Networks and Systems had a cite score of 1.0. These findings support the earlier assertion on the multi-disciplinary nature of the fintech subject, which is researched across various disciplines. Additionally, publishers of choice grow with time which assists researchers in evaluating the research output significance.

SJR assists researchers in identifying influential journals in a specified discipline. Also, it helps in assessing the quality of journal citations (Jain et al. 2021). SNIP assists researchers in comparing journals more accurately, especially those within multi-disciplinary research. Collectively, these metrics assist researchers in making a decision on which publication to choose and the potential impact based on the previous metrics. As shown in Table 1, besides the journals IEEE Transactions on Engineering Management and PLoS ONE, which had SJR scores of 1.134 and 0.803, respectively, the others had relatively low SJR scores. Similarly, these results apply to the SNIP. Thus, both SJR and SNIP indicate the multi-disciplinary nature which can entail citations not domiciled in one field. Also, the subject is a niche area which is still emerging and growing. These findings indicate the silent drivers of banking transformation which has seen the shift from banking 1.0 to banking 5.0.

3.1.6. Most Productive Authors

Table 2. Most productive authors in India and China.

Author	Total Publications	h-Index	Institution	Country
Balusamy, Balamurugan	2	31	Shiv Nadar Institution of Eminence	India
Garg, Vikas	2	10	Symbiosis International	India
Goel, Richa	2	11	Symbiosis Centre for Management Studies,	India
Kiran, Ravi	2	21	Thapar Institute of Engineering & Technology	India
Luthra, Sunil	2	76	All India Council for Technical Education	India

shows the top five authors in the subject under study. The authors were categorized based on the total publications, h-index, institution, and country. All the authors had a total of two publications in relation to the subject under study and were based in India. This shows the extent of research publication in India within the fintech domain. Accordingly, the results concur with the findings reported on research output by country (Figure 2), where India produced 57% of the articles.

The h-index is used to gauge the significance of a researchers scholarly output. Additionally, it is used as a good indicator of determining future academic productivity (Clausen et al. 2025).

Table 3. Top 5 articles cited articles.

Name of Article	Year Published	Citations-Scopus	Citations- Google Scholar	Field Weighted Citation Impact (FWCI)	Authors Countries
A review of Blockchain Technology applications for financial services	2022	263	342	8.84	India
Current landscape and influence of big data on finance	2020	177	256	6.65	Poland and China
Contextual facilitators and barriers influencing the continued use of mobile payment services in a developing country: insights from adopters in India	2020	115	165	6.84	India, Canada, and USA
Proactive user-centric secure data scheme using attribute-based semantic access controls for mobile clouds in financial industry	2018	92	122	5.26	China and USA
Mobile payments in India: the privacy factor	2019	17		1.53	India and USA

shows that the top five authors have an average h-index of about 30, which indicates a high degree of scholarly influence. The fact that much of their research is regularly mentioned highlights the importance and quality of their contributions to the field. The large number of citations indicates that these scholars are advancing important issues in the fintech space. Significantly, it contributes to the knowledge of fintech’s effect on traditional banking models.

3.1.7. Most Influential Articles

Of the publications, the five most prominent publications based on the citations are shown in Table 3. Firstly, the research output entitled A review of Blockchain Technology applications for financial services by Javaid, Haleem, Singh, Suman, and Khan. The authors were all from India but different institutions. The article was published in 2022, and has 263 citations and an FWCI of 8.84. Of the citations, a comparison was performed with Google Scholar, where Google Scholar indicated 342 while the Scopus database had 263. This indicates that the article has more citations, despite Scopus statistics showing the article has been influential as revealed by the FWCI of 8.84. Consequently, the publication has been cited more than expected according to the global average of similar publications.

Secondly, output by Hasan, Popp, and Olah entitled Current landscape and influence of big data on finance published was in 2020, and has an FWCI of 6.65. The article had 177 citations on Scopus while Google Scholar highlighted 256 citations. This shows that solely relying on the Scopus database can be a limitation. The authors were from Poland and China. In terms of citations, using Google Scholar a total of 256 citations were recorded, showing more citations than Scopus database. Alongside the FWCI of 6.65 this shows that the article had great impact, as evidenced by citation numbers which are higher than the global average for similar publications.

Thirdly, scholarly research entitled contextual facilitators and barriers influencing the continued use of mobile payment services in a developing country: insights from adopters in India by Pal, Herath, De’, and Rao was published in 2020. The article had 115 citations recorded by the Scopus database and an FWCI of 6.84. Comparing the citations, the Scopus database had 115 citations, with Google Scholar recording 165 citations. The authors were from India, Canada, and the USA. The FWCI of 6.84 clearly indicates greater influence of the article.

Fourthly, the output by Qiu, Gai, Thuraisingham, Tao, and Zhao entitled proactive user-centric secure data scheme using attribute-based semantic access controls for mobile clouds in financial industry was published in 2018. The article had an FWCI of 5.26 and 92 citations recorded by the Scopus database. Comparatively, Google scholar had 122 citations, with the Scopus database having 92 citations. The authors were from China and the United States. An FWCI of 5.26 reflects greater influence in the field.

Finally, the scholarly research entitled Mobile payments in India: the privacy factor by Sinha, Majra, Hutchins, and Saxena was published in 2019. The article had an FWCI of 1.53 and 17 Scopus database citations. Google scholar database had 159 citations, with the authors being from India and the USA. Notably, the huge difference in citations by Google Scholar and the Scopus database could possibly indicate limitations associated with the reliance on one database. Despite the few citations recorded by Scopus, the article had an FWCI of 1.53, which indicates greater influence. Compared with the other four articles, the FWCI performance is below expectations given that this was published in 2019.

To sum up, the top five articles were dominated by authors from India, who had three articles, and those from China had two articles. Noticeably, these articles were co-authored with researchers from other countries which enabled the cross-sharing of intellectual knowledge and increased the research-domain influence. Moreso, the high FWCI scores indicate greater influence on the subject of emerging risks in the fintech era. Bahmanabadi et al. (2023) argued that using FWCI in evaluating researchers’ performance is more reliable than other indexes. These results demonstrate the field’s growing scholarly influence and relevance as well as the capacity to generate significant, high-caliber research that influences theoretical frameworks, policy, and real-world banking applications. Also, providing substantial contribution to the understanding of how fintech changes traditional banking models and addressing emerging issues such as fintech-driven efficiency, financial inclusion, and digital banking.

Governance structure of universities or companies shapes the institutional decision-making and management (Yang 2023). According to India’s Department of Higher Education report covering 2021–2022, the country had 1168 universities, with 59% being state owned and 41% being privately owned (Sharma and Kaldewey 2025; Department of Higher Education 2022). In China, there were 3013 accredited universities in 2022, with 75% being public universities and 25% private universities (Tian and Liu 2025). The ownership structure of universities in China and India has a contributory factor to the country’s research output. Higher education remains a necessity for any country and holds the power to serve and benefit the nation as well. This can explain why most companies in China are state owned with substantial financial support and a mission centered on serving the national needs.

3.1.8. Overlay Visualization of Author’s Country Collaboration

The review of information concerning the author’s collaboration which enables the cross-sharing of knowledge was visualized by Vos viewer, as shown in Figure 5 and Figure 6. The darker circle shades indicate the countries with the largest volume of research output in the fintech space. In this case, China and India are the countries of study. For smaller circles the shades indicate the collaborating partner countries.

The authors in China collaborated with authors in the United States and India. These articles were mainly written in 2021. Authors in India had articles published in 2022, and they collaborated with authors in the United Kingdom and China. Notably, the collaborating countries of China, India, the United States of America, and the United Kingdom are coincidentally the leading countries in fintech.

Collaboration facilitates the exchange of knowledge and skills among researchers. This thereby enhances learning and the development of better and enhanced methodological research approaches. Duan (2024) argued that studies involving collaboration are crucial for academic advancement because they enable researchers to pool their resources and expertise to produce novel concepts that might not have been achievable through individual effort. As a result, individuals may work more productively and provide better outcomes. This could be the reason why the researchers collaborated, which reflects knowledge sharing.

3.1.9. Theoretical Application to the Bibliometric Findings—Performance Analysis

The three theoretical frameworks, institutional theory, information asymmetry, and holistic digital maturity model, are useful in explaining the bibliometric results. For instance, as shown in Section 3.1.2, India has more fintech-related research outputs compared to China. These different results can be attributed to country-level differences. Characteristically, China’s academic research structure is state-driven which discourages open intellectual conversations, while India’s structure is open and the market-oriented academic policy environment fosters collaborations.

Pringle and Woodman (2022) argued that China’s academia and researchers are between a rock and a hard place as they are subjected to state controls and censorships. This limits idea generation and international collaborations. By applying the information asymmetry theory to China, this highlights how unequal access to information and technology leads to disparities in innovation, research, and market participation. Also, HDMM demonstrates that China’s rapid expansion entails limited researchers access to sensitive or proprietary data. Accordingly, some Chinese research universities have adopted integrated approaches in response to managing the interaction of institutional and national factors (Liang et al. 2024). These approaches have enabled higher education institutions to maintain credibility and a competitive edge.

India has an open, diversified, and market-oriented academic policy environment. Sanjeev (2025) observed that neoliberal reforms in India’s higher education system have seen a significant shift in the structure and dynamics, changing it from a state-controlled to a market-oriented approach. As a result of these policies, access and participation have significantly increased. Additionally, the One Nation One Subscription (ONOS) operationalized in January 2025 with the goal to reduce research access disparities and enhance research productivity (Mahanand and Naik 2025). This increased data accessibility minimizes informational access challenges, thereby encouraging international collaboration. This resonates with institutional theory and HDMM in that institution factors shape the environment, with them interacting gradually to determine the resilience and risk profile of fintech ecosystems at different stages. Hence the reason for India being the leader in fintech-related issues by producing a wealth of related research opportunities.

3.2. Science Mapping

Science mapping is an analytical technique that shows the current knowledge landscape within a certain topic and graphically represents its inter-relationships (Donthu et al. 2021). Two bibliometric analysis approaches were employed with the VOSviewer version 1.6.18 for the scientific mapping of emerging risks in fintech research in China and India, which answers research question 2. Firstly, we undertook the identification of e-key words or word clouds to better understand the main themes that define database research during the periods of the study. Secondly, the core themes influencing the intellectual framework of the database researched were revealed via a network analysis based on key word co-occurrence.

3.2.1. Key Word Analysis

The use of key word analysis as part of the scientific mapping is answering research question 2. For the key word analysis, Scival and Vos Viewer tools were used in gathering information.

Based on the emerging risks in the fintech-driven banking research, Figure 7 shows the word cloud of the key phrases commonly referred to in publications in China and India. These are computed based on the author’s key words of the scholarly output’s abstract. These key words show the growth and relevance over the period 2015 to 2024, indicating the multi-disciplinary evolutionary nature of the discipline. This is in line with the integration of information technologies with banking transformations from banking 1.0 to banking 5.0. Additionally, this transformation brings in associated risks as evidenced by Figure 8 which shows risks management elements. These elements include risks, finance, artificial intelligence, blockchain, big data, and information management.

Figure 8 and Figure 9 shows the visualized key words with risk management, artificial intelligence, and information management being emerging risk issues in China and India. These results indicate a growing trend towards addressing the risk management aspects in the fintech or digitalized environment. The fintech environment is associated with blockchain, big data, artificial intelligence, information management, and emerging technologies. Thus, more effort should be focused on addressing emergent risk issues.

In terms of growth over the period 2015 to 2024, the key phrases for the study are shown in

Table 4. Top five growth key phrases, 2015 to 2024.

Key Phrases	Growth %
Industry	1800
Risk Management	1200
Emerging Technology	1100
Emerging Economies	700
Emerging market	200

. Of the key phrases or words, industry had an 1800% growth, followed by risk management with a 1200% and emerging technology with an 1100%. As seen, emerging technology relates to financial technologies. China and India have been classified as emerging economies within the continent of Asia (IMF 2025).

As shown in Table 4, risk management has grown by 1200% over the period 2015–2024. This indicates the dominance of cybersecurity, operational, ethical, and societal risks in fintech, reflecting the technological, regulatory, and socio-political contexts of different markets. Cybersecurity risks are heightened in environments of rapid digital adoption coupled with uneven data protection, leaving systems vulnerable to fraud and breaches. Operational risks emerge from the complexities of scaling digital financial platforms and integrating new technologies, while ethical and societal risks intensify as fintech reaches underserved populations, raising concerns over consumer protection, data privacy, and inclusion. These risks are dynamic, interacting and escalating as innovations such as artificial intelligence and blockchain transform service delivery and user interactions, thereby magnifying regulatory challenges and the demand for adaptive governance.

These findings indicate that risk management continues to dominate and emerge within banks or financial institutions. Evidently, this can be an indicator of the risky nature of the financial services sector due to the regulatory environment they are accustomed to. Also, the domination reflects the banking sector’s shift towards technology-driven models. Hence, the increase in digital banks with some collaborating with fintech to offer seamless customer service. This calls for appropriate regulatory responses which do not stifle innovation.

The theories, information asymmetry and institutional, identified in the study point to the issue of information asymmetry among stakeholders, and together with the growth institutions this should evolve in the same way as risk management and technological innovations are evolving (Doe et al. 2023; Scott 2004; Auronen 2003). The holistic digital maturity model emphasizes addressing the managerial and cultural risk issues in the digital transformation process (Aras and Büyüközkan 2023). Interestingly, China and India have young tech savvy generations and supportive governments to digitalize the economy.

Findings in Section 3.2.1 indicate the substantial potential of fintech in addressing inefficiencies associated with traditional banking models. With the transformation into digital banking induced by technological innovations and consumer choices, a financial regulator’s mandate, such as financial inclusion, can be attained. This has been observed in India, with the country’s financial inclusion index of 67 recorded for the year to March 2025 (RBI 2025). In China, the big tech firms, such as Baidu and Alibaba, provide small loans for online shop owners and shoppers from their e-commerce platforms through micro-lending companies. Despite the increase in digital financial access, this has brought associated risks such as cybersecurity and privacy. To promote innovation while preserving financial stability and consumer safety, regulatory interventions such as regulatory sandboxes, licensing policies, and innovation hubs should be spearheaded. Interestingly, with banking 5.0 taking shape, this study offers insights to regulators, policymakers, industry stakeholders, and researchers within China and India to ensure that as banking advances the risks which arise are acknowledged, calling for a balanced response regarding the regulator’s mandates.

China’s centralized oversight and fintech regulations accelerate adoption, while India’s multi-regulator system places emphasis on openness and compliance. Disparities in fintech research and market conditions can be explained by variations in institutional frameworks and regulatory environments which influence innovation pathways. India has strong legal cybersecurity regulations and technical capabilities. This can explain why cybersecurity risk management in India is more balanced and cooperative while in China it is more secretive and imposes strict penalties on defaulters.

3.2.2. Visualizing Key Clusters and Themes of Emerging Risks in Fintech—China and India

Another notable bibliometric method for analyzing the frequency and connection between important terms in scholarly literature is co-occurrence analysis. This technique assists in identifying essential research subjects, new trends, and the general organization of a certain field of study by mapping author key words that commonly occur together (Chang et al. 2015). Co-occurrence analysis has been utilized in the present study to examine the emerging risks in fintech-driven digital banking, highlighting the associated topics and significant concepts in the scholarly discourse. Two separate topic clusters, each representing a major area of attention within the literature, are revealed by the Vos Viewer analysis of the research landscape on the developing threats in fintech on banking (Figure 10). Together, these clusters encompass the range of academic research on the subject and offer a framework for explaining the various ways in which fintech impacts banking in China and India.

Cluster 1: Fintech in India (Red Cluster)

Cluster 1 focuses on fintech issues in India. The themes include technology, management, department, and study. Thus, the research examines India’s academic research into technology policy and its incorporation into the economy. Key examples of these studies include blockchain technology application for the financial services sector (Javid; Mobile payments in India: the privacy factor (Sinha et al. 2019); and determinants of mobile apps adoption by retail investors for online trading in emerging financial markets (Nair et al. 2023). Of the reviewed articles, 63% are accounted for by this cluster, demonstrating that the fintech research in India looks mainly at addressing emerging risks within the financial services sector.

Cluster 2: Fintech in China (Green Cluster)

This cluster focuses on studies relating to China. The themes being school, economics, department, management, and technology. China’s cluster accounts for 37% of the reviewed articles. Articles researched include the study by Yu (2024), which examined the issues in China related to commercial bank risks, technology innovation, and stability. Another study by Wang et al. (2024) investigated the data privacy risks and cybersecurity risks in the banking sector’s digital transformation journey. These studies enhance knowledge dissemination and the discovery of fintech solutions within China regarding the emerging risks.

To ensure equitable and responsible fintech adoption, regulatory interventions ought to focus on customer protection, cybersecurity, and digital financial literacy in China and India, where fintech is growing fastest. To compete with or work with fintech companies, banks need to strategically rethink their operational structures. To improve customer satisfaction and service efficiency, traditional financial institutions may leverage open banking frameworks, forge strategic partnerships with fintech startups, or develop their own proprietary fintech solutions. These initiatives are activities under banking 5.0. Failure by traditional banks to embrace digital banking models, as advanced by holistic digital maturity model, will mean they will lose market share as consumers seek convenience. Steps have been taken some Asian countries to license only digital banks. For instance, in 2020 four digital bank licenses were issued in Singapore, but no others have been issued in Singapore, while Malaysia issued four digital bank licenses in 2022 (Bank Negara Malaysia 2022; Monetary Authority of Singapore 2020).

In summary, the graphic visualization by Vos Viewer depicts the multifaceted effects of fintech on banking, including trends in user acceptance, competition, emerging risks, technological innovation, and financial stability. The strong connections across clusters highlight how fintech research is multi-disciplinary, spanning consumer behavior, finance, economics, and financial technology. This supports the findings in Section 3.1.3. Fintech serves as a focal point, connecting innovation to the banking industry’s potential and problems. A wider and changing conversation in the sector is indicated by the increased interest in fintech adoption and regulatory aspects, even though research on risk and competitiveness is still very important.

The bibliometric findings categorized into performance analysis and scientific mapping revealed an intricate interplay existing between fintech evolution and regulatory frameworks. As fintech advancements outpace traditional regulatory frameworks, dynamic regulations should be advanced. Deb (2025) proposed that these regulations should strike a balance between promoting innovation and maintaining financial stability and consumer protection. Additionally, this draws attention to ways that regulators can use innovative technologies to address traditional regulatory challenges. Thus, the dynamic digital banking environment should foster sustainable fintech growth while minimizing emerging risks (Nourahmdi and Rasti 2025).

The comparative bibliometric performance of India and China further illustrates the institutional dimensions of fintech development. Despite China’s more mature fintech ecosystem, India demonstrates stronger research output, reflecting a collaborative academic ethos, openness to international partnerships, and more flexible regulatory framework. This divergence shows that bibliometric indicators capture research intensity rather than ecosystem maturity or market scale. It also highlights how regulatory and institutional contexts shape both innovation trajectories and risk profiles. Placing these insights within broader debates on fintech regulation and financial stability underscores the need for policymakers in emerging markets to strike a careful balance between fostering innovation and safeguarding against systemic risks.

4. Methodology

A bibliometric analysis approach using databases such as Scopus and Google Scholar with tools like VOS viewer and R has been adopted by many researchers due to its cross-disciplinary information repository (Patvardhan et al. 2025; Passas 2024; Doulani 2020). Additionally, it provides an enhanced scholarly foundation to discover emerging scholarly trends. The research relied on the Scopus search database.

The Scopus database was chosen as the database provides access to a vast number of internationally recognized publishers and peer-reviewed journals, possessing robust citation analysis and tracking tools, among others (Mohamad 2025; Chadegani et al. 2013; Bergman 2012). The tracking tool aids in evaluating the significance and scope of research outputs by different researchers globally.

In addition, the researcher adopted Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol, which places major emphasis on the entire literature review process. Ungratwar et al. (2025) argued that SPAR-4-SLR was mainly applicable to diverse and emerging disciplines. Likewise, this study is centered on emerging risks in the fintech-driven banking environment, which is an emergent and multi-disciplinary subject. The SPAR-4-SLR involves three review stages, namely scholarly output collection, organization, and evaluation (Ungratwar et al. 2025; Paul et al. 2021). Other approaches not considered were the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, which concentrates on reporting, while the Theories, Contexts, Characteristics, and Methodologies (TCCIM) approach is theory driven.

Table 5. Systematic review using SPAR-4-SLR protocol.

Gathering	Search Database: Scopus
	Search key word: “Financial Technology” OR “FinTech” AND “Emerging Risks”
	Search result: 1257
Arranging	Organizing Filters: Year, Subject area,
	Filtered Year for inclusion: 2010 to 2024
	Subject Area Exclusion: Nursing, Veterinary, Dentistry, Chemistry, Physics and Astronomy, Engineering,
	Filtered publication stage inclusion: final
	Filtered by country for inclusion: India and China
	Filtered by language for inclusion: English
	Filtered search result: 167
	Final search result after manual data cleaning: 162
Assessing	Analysis method: Bibliometric analysis techniques: “Performance analysis”—publication trend, most contribution authors, sponsors, journal, Science mapping—Analysis using word clouds and network analysis using key word co-occurrence
	Reporting format: Tables, graphs, and visual maps
	Limitation: Scopus bibliometric data accuracy and completeness

Sources: Author’s compilation.

shows an overview of the entire review process using the SPAR-4-SLR protocol. The protocol demonstrated a methodical and thorough approach to gathering, assessing, and identifying essential scholarly publications. Though multiple databases are encouraged, challenges exist in the integration and exporting of these datasets to the bibliometric. The reason being is that the datasets are designed differently, which can be difficult for further analysis.

Below is the explanation of the process flow undertaken:

4.1. Assembling

In terms of searching the relevant literature on the subject, key words should be identified and combined to produce relevant results (Chaudhary 2025; Alkhowaiter 2020). Further, Mohamed et al. (2025) posits that to enable a through exploration of literature across the database, key words should be systematically combined by applying the Boolean operators “OR” and “AND”. Boolean operators were applied with the key words and the following search string was used: financial technology or fintech AND emerging risks or risks.

After the identification of key words, a comprehensive search of the scholarly publications was performed using specified search strings within the article title, abstract, and key words sections of the Scopus database. Chaudhary (2025) concurred with Comerio and Strozzi (2019) that Scopus was the largest scholarly publications database with citations and abstracts of peer-reviewed literature. Therefore, this database was used as it has numerous tools and benefits such as ease of access, global reach, and citation analysis, among others. The initial search result yielded 1257 articles.

4.2. Arranging

Bibliometric analysis requires careful attention to data quality and coverage. Institutional differences, such as India’s open academic environment versus China’s state-driven model, can create database biases that under-represent certain countries’ outputs. The researchers implemented rigorous data cleaning and deduplication protocols to ensure data collected were consistent in terms of author names, affiliations, journal names, and key words, among others.

To sift through the 1257 articles gathered from the assembly stage, the researcher utilized the filters. The filters enabled the exclusion and inclusion of articles based on the year, publication stage, subject area, and language. Nian and Said (2025) agree with Paul et al. (2021) that the filtering of articles enhances the reliability and credibility of the results. Though, duplicate records affect reliability (Patel and Patel 2025).

Another criterion was applied to refine the search process. Firstly, the time-period was restricted to 2010 to 2024. This approach ensured the retrieval of a comprehensive and focused body of literature to review for subsequent analysis (Haruna et al. 2025; Bajwa et al. 2022). The selected period of 2010 to 2024 was chosen as the period would give a better perspective of past, present, and possible future research concerning the emerging risks in a fintech-driven digital banking environment. Secondly, the subject area excluded those related to medical sciences, physics, veterinary studies, energy, and pharmaceutical studies, among others. Thirdly, only articles at the final publication stage were considered as they would have gone through a strict peer-review process. Fourthly, press articles were not considered because of varied editorial policy issues. Fifthly, inclusion of articles published in India and China. Sixthly, non-English articles were excluded owing to language limitations. Surprisingly, articles in most Asian countries were mainly in English. After the screening, 167 articles remained. These articles were manually selected to avoid utilizing articles that are outside the financial services sector or banking sector. Five articles which focused on sports, energy trading, animal fattening, and solar usage were excluded. Finally, 162 articles were considered for the next stage of assessment.

4.3. Assessing

Bibliometric analysis was performed on the remaining 162 articles. The results were presented under the subheadings Performance Analysis and Science Mapping. The final list of 162 articles was then exported for further analysis using software tools such as Vos viewer and Scival. These tools assisted in developing conceptual maps, topics, and top clusters, among others. After retrieval of exported data, the data underwent a further rigorous analysis and visualization process.

In summary, data was extracted from the Scopus database using the query words with inclusion and exclusion criteria applied. Data was cleaned by analyzing the exported data in Excel through checking and removing any duplicate records, reducing the total to 162 articles. Also misclassified articles and off-topic articles were removed after a thorough checking process. Data analysis tools such as Vos viewer and Scival were applied to the 162 articles. The next section presents and discusses the results.

5. Conclusions

The bibliometric analysis undertaken reveals research on emerging risks in the era of fintech-driven digital banking in China and India. Data was extracted from the Scopus database using the query words with inclusion and exclusion criteria applied, resulting in 162 articles being analyzed. Key findings revealed importance of addressing risks such as risk management, emerging technologies, big data, artificial intelligence, and blockchain, which are the study’s key phrases. Also, the subject is multi-disciplinary, as shown with the research output in various disciplines. Research output is inclined to India rather than China, which is despite China domiciling some big tech firms. Comparatively, India dominates on the performance analysis metrics compared to China. The graphic visualization by Vos viewer depicts the multifaceted effects of fintech on banking, including trends in user acceptance, competition, emerging risks, technological innovation, and financial stability. The strong connections in both countries across clusters highlight how fintech research is multi-disciplinary, spanning consumer behavior, finance, economics, and financial technology. This underscores the collaborations driven in banking 4.0 and 5.0 evolution.

For financial institutions, the shift from traditional banking to digital banking marks an important industry transformation. For this reason, digital banks are being licensed under strict regulations while the fintech environment has forced traditional banks to be innovative and adaptable. Despite emerging risks evolving, a paradigm shift is needed by stakeholders in the financial ecosystem. Considerably, banks which embrace the fintech operating environment remain well-positioned to prosper in the rapidly evolving financial environment.

To mitigate emerging risks and foster innovation, especially in banking 5.0, collaboration involving policymakers, stakeholders, and academics remains essential. Future studies should concentrate on creating robust risk management frameworks that address the ethical issues associated with financial technologies. In the fintech era, this can ensure a robust and inclusive digital financial services ecosystem. Additionally, more bibliometric geographical-centered studies can contribute to increased research output and the design of customized solutions.

6. Limitations

The Scopus database extracted can have undefined records which might affect data completeness (Liu and Wang 2025). In this study, the researcher had to physically cross-check the records’ classification and correctly assign the associated record, which can be country status and publication source. Also, bibliometric data is dynamic as it is constantly updated, hence the need to revisit the study’s conclusions which might change when additional knowledge becomes available. However, periodic updates are encouraged to maintain the study’s relevance. Furthermore, it is crucial to remain cognizant that bibliometric analysis might not fully capture current patterns, particularly when it comes to recently released works that have not yet received many citations. Citations captured by Scopus are always low, as evidenced by Google Scholar citations which were higher.