research paper on open banking

Open banking: an early review

Journal of Internet and Digital Economics

ISSN : 2752-6356

Article publication date: 5 July 2024

Issue publication date: 31 July 2024

This paper offers an overview of the burgeoning literature on open banking, focusing on its implications for the financial sector.

Design/methodology/approach

The paper reviews the recent developments in the nascent literature of open banking. In particular, it discusses the following issues. (1) the extent to which open banking fosters competition, drives innovation and enhances financial inclusion; (2) the impact of institutional arrangements on the outcomes of open banking initiatives and (3) the critical role of government in promoting open banking and regulating banking activities.

The paper concludes with a discussion on potential directions for future research. First, open banking introduces significant challenges to the traditional banking model. Furthermore, the interplay between open banking and financial risk presents an area ripe for exploration. Lastly, the importance of consumer education in the context of open banking cannot be overstated.

Originality/value

Open innovation enables financial institutions generate productive innovations as well as provide customers with significantly better services, by getting access to previously restricted customer data. However, currently non-bank and fintech lenders often face significant barriers in accessing comprehensive customer data, which restricts their capacity to support non-standard credit models. More emphasis is required to be assigned to research on the economic impact of open banking.

• Open banking
• Bank regulation
• Regulatory arbitrage
• Financial innovation

Xie, C. and Hu, S. (2024), "Open banking: an early review", Journal of Internet and Digital Economics , Vol. 4 No. 2, pp. 73-82. https://doi.org/10.1108/JIDE-03-2024-0009

Emerald Publishing Limited

Copyright © 2024, Chengbo Xie and Sijia Hu

Published in Journal of Internet and Digital Economics . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Open Innovation means that valuable ideas can come from inside or out- side the company and can go to market from inside or outside the company as well.

Contrary to the traditional model of closed innovation, which relies exclusively on internal research and development efforts for innovation, open innovation posits a paradigm shift. The underlying premise of open innovation is the recognition that valuable ideas and innovations often originate outside established organizations, particularly from startups. This necessitates the adoption of inbound open innovation strategies to assimilate external knowledge, and outbound open innovation strategies to leverage internally developed innovations that are not being utilized to their full potential. Since the publication of Chesbrough (2003) , the concept of open innovation has gained significant attention across both academic research and practical business applications [1] . This paper aims to introduce and examine a specific transformation within the banking sector that embodies the principles of open innovation: open banking.

Imagine you want to use a financial product offered by an organization other than your bank. This product could be anything you feel would help you, such as an app that gives you a full picture of your financial status, including expenses, savings, and investments or it could be a mortgage or line of credit. But for this product to be fully useful to you, it needs in- formation from your bank, such as the amount of money you have coming in and going out of your accounts, how many accounts you have, how you spend your money, how much interest you have earned or paid, etc. You then instruct your bank to share this information with this other institution or app. Should you wish to stop using this product, you can instruct your bank to stop sharing your data at any given point in time, with no strings attached. This concept is called open banking.

From the perspective of open innovation, newcomers to the banking industry, such as fintech companies, are more likely to generate productive innovations compared to incumbent entities like traditional commercial banks, mainly because those companies have more advanced data processing technologies. However, as summarized by Babina et al. (2024a , b) , non-bank and fintech lenders often face significant barriers in accessing comprehensive customer data. This limitation restricts their capacity to support non-standard credit models, leading them to frequently rely on standardized models for originating residential mortgages. In contrast, traditional banks can leverage their extensive customer data to employ more customized, non-standard credit models. Thus, should non-banks and fintech companies gain access to previously restricted customer data, they could provide customers with significantly better services.

In this paper, we will review the recent developments in the nascent literature of open banking. In particular, we will discuss the following issues.

In Section 2 , we ask whether customer data should be shared among financial intermediaries. An examination of whether customer data should be disseminated among financial intermediaries is imperative, necessitating a comprehensive analysis of the benefits and drawbacks associated with data sharing practices within financial institutions. Numerous instances exist where augmented access to data significantly enhances the services offered by fintech companies, provides a better chance of valuable information production, reduces forecast errors or potentially elevates the competitive dynamics within the banking sector. However, there are circumstances where confidentiality is preferable, as the generation of information might infringe upon the “no questions asked” state posited by Holmstrom (2015) , thereby inducing unnecessary information asymmetry. This principle highlights the potential inefficiency when private data are unnecessarily disclosed, suggesting that carefully deciding when and what customer data to share (or not to share) among financial intermediaries is crucial. Moreover, open banking challenges traditional relationship banking by opening data access and diminishing banks’ competitive edge from exclusive customer information. Simultaneously, it could alter the reliance on collateral in financial transactions. These developments necessitate an in-depth examination of their impact on social welfare.

In Section 3 , we ask whether open banking is the optimal solution for data sharing. The open banking framework empowers customers by granting them the rights to their data, presenting a notable departure from traditional models. This approach directly addresses the question of data ownership, which is a thriving literature. In a frictionless environment, the Coase theorem posits that the specific allocation of data ownership is immaterial to achieving efficiency, as parties are theoretically able to negotiate the transfer of rights to those who can most effectively harness the data. This suggests that, in an ideal scenario, data would seamlessly flow towards those entities capable of deriving the greatest value from it. Nonetheless, in the practical context of real-world frictions, the ownership of data could be crucial. If banks are allowed to retain exclusive control over data, they may be motivated to hoard this valuable resource, thus avoiding competition and fostering a data monopoly. This consideration strongly supports the argument forgiving data ownership to consumers themselves. Yet, this approach is not without challenges, particularly when customer data are highly correlated, potentially leading to a coordination dilemma. Such a situation is fraught with the risk of individuals underselling their data due to concerns that others may do so first, precipitating a race to the bottom. This dilemma is characterized by the risk of individuals selling their data at suboptimal prices due to fears that others might preemptively do the same, leading to a race to the bottom. Thus, it requires comprehensive analysis in determining the optimal allocation of data ownership, especially in the context of advocating for open banking as a model for facilitating data sharing.

In Section 4 , we seek the role of government: can government policies enhance efficiency and mitigate the challenges associated with open banking? As previously discussed, financial intermediaries typically exhibit minimal motivation to share data with competing institutions, indicating a potential need for government intervention to promote an open banking ecosystem. Moreover, the prospect of open data access raises significant concerns regarding the current regulation practice, including excessive risk taking and regulatory arbitrage. Additionally, similar to the discussions on credit registries, the choice between utilizing private versus public agencies for data sharing can markedly affect the open banking framework’s effectiveness and security. Therefore, government policies and regulatory measures are crucial not only in facilitating data sharing among financial entities but also in establishing the optimal infrastructure for such exchanges to safeguard consumer interests and ensure system-wide efficiency.

Section 5 concludes the paper by summarizing key findings and proposing avenues for future research. As it continues to evolve, the burgeoning literature on open banking and its implications presents numerous opportunities for further investigation. There remains a lot of intriguing research questions to explore and theoretical predictions to empirically test. This section will outline potential directions for future research, highlighting areas where the current body of knowledge can be expanded to deepen our understanding of open banking’s impact on the financial sector and beyond.

2. Open banking: is data sharing welfare improving?

Open banking fundamentally empowers bank customers with the autonomy to choose whether their data is shared with alternative financial service providers. At its core, open banking is designed to facilitate data sharing, embodying a classic narrative in information economics. By granting lenders access to more comprehensive data about borrowers, including credit and payment histories, open banking enables a more accurate assessment of credit risk. This enhanced information access aims to mitigate the adverse selection problem, a challenge where lenders are unable to distinguish between high-risk and low-risk borrowers due to imperfect information. This theory, pioneered by Jaffee and Russell (1976) and Stiglitz and Weiss (1981) , posits that such information asymmetries can lead to credit rationing, where potential borrowers are denied access to credit not because of their creditworthiness, but due to the lenders’ inability to accurately assess risk. Open banking seeks to address these inefficiencies by improving information flow between parties, potentially reducing the incidence of credit rationing in the financial sector.

In particular, aligned with the principles of open innovation, empirical research has underscored the efficiency gains non-bank financial institutions and fintech companies bring to the credit market. Fuster et al . (2019) demonstrate that fintech firms incur lower processing costs than traditional lenders, without incurring higher default rates. Similarly, Tang (2019) and Gopal and Schnabl (2022) provide evidence of the complementary role played by P2P lending platforms and fintech companies, particularly in the origination of small loans. Thus, granting non-bank and fintech entities access to traditional bank customer data – a core idea of open banking – is anticipated to further enhance financial service accessibility. Supporting this idea, Babina et al. (2024a , b) present empirical evidence from the UK, illustrating how open banking facilitates consumer access to a broader array of financial services, including financial advice and credit. Notably, their findings also suggest that open banking aids SMEs informing new relationships with fintech lenders. Complementarily, Nam (2023) uses loan data from a leading German fintech lender to show that open banking and data sharing contribute to more efficient credit allocation and the mitigation of adverse selection.

Theoretically, aligning with empirical observations, Babina et al. (2024a , b) study the role of open banking as a catalyst for innovation, drawing on concepts from both industrial organization (IO) and finance literature. Using a calibrated model, their analysis reveals that open banking facilitates welfare improvements through the dual channels of market entry and product improvements when shared data is used for advice. Furthermore, when data sharing is used for credit, it fosters additional market entry and stimulates competition by mitigating adverse selection issues. However, this positive impact is somewhat moderated by increased costs borne by consumers who are either privacy-sensitive or inherently more expensive to serve, indicating a nuanced trade-off between the broad benefits of open banking and its implications for specific consumer segments.

Other theoretical works provide ambiguous predictions. He et al . (2023) and Goldstein et al . (2022) contribute to this discussion by building on the model proposed by Broecker (1990) , which highlights how credit market lenders employ independent but imperfect screening tests to evaluate a borrower’s repayment capability. A key aspect of Broecker’s model is the “winner’s curse” problem, suggesting that a lender acquiring a borrower may have missed negative signals detected by other lenders, with the curse being less severe for lenders possessing better screening abilities.

Expanding on this model, He et al . (2023) distinguish between banks and fintech companies based on their screening capabilities. They argue that in a traditional banking environment, banks enjoy a screening advantage due to their exclusive access to customer data, while fintech firms, despite their technological edge, are limited by the absence of such data. Open banking’s data-sharing provisions could significantly enhance fintech companies’ screening abilities, narrowing the competitive gap with banks. This narrowing fosters increased competition; however, an overcorrection could lead to dominance by fintech firms, creating a situation of reduced competition and potentially worse-off borrowers.

Goldstein et al . (2022) conducts similar analysis under the assumption that lenders possess identical data-analytic capabilities, arriving at similar conclusions as well. Yet, the distinction lies in the focus areas: He et al . (2023) underscore the significance of customer choice in data sharing, whereas Goldstein et al . (2022) delve into how open banking intersects with aspects of maturity transformation. This nuanced difference highlights the complicated impacts of open banking on the credit market, suggesting areas for further empirical investigation.

All these analyses presume that intensifying competition is good for the financial system. However, a large strand of literature focuses the possibility that bank com-petition may result in financial instability (See, e.g. Keeley, 1990 ). This is because the increase in competition would cause bank charter values to decline, which in turn forces banks to increase asset risks and reduce bank capital. Right now, the literature on open banking is silent on this issue. However, it may produce novel insights if one can combine financial instability with the current open banking trend as policymakers would definitely be interested in it.

While existing analyses predominantly posit that heightened competition, as facilitated by open banking, benefits the financial system, a significant body of literature suggests that increased bank competition may lead to financial instability (e.g. Keeley, 1990 ). The rationale is that escalated competition diminishes bank charter values, compelling banks to adopt riskier asset strategies and reduce their bank capital. Currently, discussions on open banking largely overlooks this potential linkage to financial instability [2] . Bridging this gap by integrating concerns of financial stability with the emerging trends in open banking could yield critical insights. Such an investigation is not only academically valuable but also of interest to policymakers, who are tasked with balancing the dual objectives of fostering innovation and ensuring system-wide stability.

The seminal works of Diamond and Dybvig (1983) and Diamond (1984) have initiated extensive research into the unique functions of commercial banks relative to other financial intermediaries. This body of literature underscores the irreplaceable role that banks play in critical financial intermediation processes, such as underwriting, monitoring and balance sheet lending. Recent studies by Gopal and Schnabl (2022) and Buchak et al . (2018) reaffirm that, despite the rapid evolution of fin-tech companies and other non-banking entities, these institutions struggle to fully substitute for banks in these key areas.

Regarding this, Babina et al. (2024a , b) contend that the importance of data in relationship banking implies that removing banks’ exclusive control over customer data could fundamentally alter the dynamics of relationship banking. Another aspect of particular interest is the potential tension between the concept of data sharing inherent in open banking and the traditional banking function of confidentiality. Kaplan (2006) and Dang et al . (2017) contribute to this discussion by suggesting that banks sometimes benefit from keeping detailed asset information confidential to avoid the creation of unnecessary asymmetric information, particularly when private information production is feasible. Thus, the emerging trend of open banking, with its emphasis on data sharing, might inadvertently conflict with the critical role of banks as guardians of sensitive information, raising questions about the balance between transparency and the efficient functioning of financial markets.

3. The institution of open banking

The open banking framework represents a significant shift from traditional banking models, primarily by empowering customers with control over their own data. This paradigm shift is at the forefront of ongoing discussions within the literature on data ownership. As highlighted by Jones and Tonetti (2020) , data distinguishes itself from other assets due to its non-rivalrous nature; it can be utilized simultaneously by multiple parties without diminishing its value. This feature suggests that the potential benefits derived from data access could be substantial, underscoring the importance of who holds the control rights over data. In particular, they show that allocating data control rights to consumers is more efficient, as firms might otherwise hoard data to inhibit market entry. Thus, giving data property rights to consumers can take more advantages of non-rival data, which is consistent with the idea of open banking.

Farboodi and Veldkamp (2021) developed a theoretical model of a data economy where the use of customer-generated data plays a pivotal role in minimizing forecast errors. In such an economy, larger firms, capable of processing extensive transaction data, are positioned to benefit more from data utilization. This prediction is empirically supported by Babina et al. (2024a , b) , who find that larger firms gain greater advantages from AI investments. Given that banks are substantial entities in the financial sector, these findings highlight a viable method to diminish the competitive disparities between traditional banks and fintech companies: by enabling customer data sharing with fintech firms. This strategy, central to the open banking initiative, aims to level the playing field and ensure fair competition within the financial industry.

On the other hand, there are situations where it is not efficient to let customers control data. Acemoglu et al . (2022) consider a model where one customer’s data reveals information about others. This creates an externality that the leakage of one user’s data weakens other users’ incentives to protect their data and privacies. This externality depresses the price of data and leads to excessive data sharing and lower welfare. Parlour et al . (2022) compare two different ways of payment data sharing: firms selling data to the lender or consumers owning the data and choosing whether to port their data to the lender. A similar data externality arises when consumers own the data, making everyone shares the data for free. Thus, policies that aim to give consumers more direct, and potentially stricter, control of their data may have the unintended, opposite effect.

On the contrary, there are circumstances where granting customers control over their data may not lead to optimal outcomes. Acemoglu et al . (2022) present a model illustrating how an individual customer’s data can inadvertently reveal information about others, generating an externality. This leakage can diminish other users’ incentives to safeguard their data and privacy, consequently depressing data prices and fostering excessive data sharing, ultimately resulting in reduced welfare. Similarly, Parlour et al . (2022) explore two mechanisms of payment data sharing: direct sales of data by firms to lenders versus consumer ownership of data with the option to port it to lenders. They find that consumer ownership introduces a negative externality, leading to widespread data sharing at zero price. Therefore, policies designed to enhance consumer control over data might result in unintended opposite effect by encouraging pervasive data sharing due to these externalities.

In the framework of open banking, the right to share or withhold data with other financial institutions rests with the customers, underscoring the significance of consumer choice. Brunnermeier and Payne (2022) demonstrate that when agents lacking collateral opt for information portability choices contrary to those preferred by lending platforms, the latter may cease offering uncollateralized lending. Contrary to prevalent beliefs, this mechanism suggests that open banking might actually restrict access to uncollateralized credit. Furthermore, He et al . (2023) make a distinction between sharing credit-quality data and customer preference data. In scenarios where fintech companies’ access and utilize customer preference data, they gain insights into privacy-sensitive information, enabling them to engage in precision marketing. This capability allows fintech firms not only to tailor their offerings more effectively but also to exclude potentially risky borrowers. If significant, such a mechanism under open banking could enhance screening processes, thereby elevating overall welfare by mitigating risk and aligning products more closely with consumer preferences.

4. The role of government

In this section, we review the existing research on the role of government in advancing open banking initiatives. The reluctance of financial intermediaries to voluntarily share data with competitors positions the government as a crucial force pushing for the open banking transformation. Babina et al. (2024a , b) note that open banking policies have been embraced by 49 countries, with numerous others taking preliminary steps toward implementation, underscoring the government’s critical role in the open banking ecosystem. Leveraging an extensive dataset, their research investigates the political and economic factors driving open banking policies. They find significant diversity in policy approaches, suggesting that the formulation of optimal open banking regulations is influenced by a myriad of factors specific to each financial system. Particularly, they identify consumer trust in data sharing with fintech companies as the main driver of open banking policy adoption. The trust from consumer is indicative of people’s readiness to engage in data sharing, a key element for the success of open banking system.

In addition, the transition to open banking introduces several regulatory challenges. First, as outlined in Section 2 , the advent of open banking generally increases competition within the banking sector, potentially leading to excessive risk-taking by banks. This may necessitate more stringent regulatory measures to curb such activities. Second, as documented by Buchak et al . (2018) , regulatory disparities have contributed to the rise of shadow banking entities, including fintech companies. The open banking transformation, by enhancing the appeal of fintech firms, could further amplify this development. Third, the variance in open banking policies across countries provides opportunities for regulatory arbitrage, complicating efforts to maintain a consistent regulatory environment. Fourth, Philippon (2019) contends that the rise of fintech companies, which rely heavily on big data technologies, may undermine the effectiveness of existing regulations designed to protect minorities. Together, these factors underscore the need for a nuanced approach to regulatory adaptation in response to the evolving landscape of open banking.

Furthermore, in instances where the efficiency benefits of open banking are not guaranteed, it is crucial for the government to recognize such situations and, where feasible, rectify the inefficiencies. Brunnermeier and Payne (2022) suggest that the introduction of a fully interoperable public ledger by the government could serve as a remedy for inefficiencies arising from privately operated open banking systems. Conversely, in scenarios described by He et al . (2023) and Parlour et al . (2022) , where the efficiency outcomes are contingent upon the magnitude of underlying economic forces, policymakers need to be careful to formulate policies.

Finally, open banking shares similarities with credit registries, making it insightful to draw lessons from the literature on credit registries. In terms of the role of government, a crucial question is whether data sharing should be facilitated through a centralized agency, such as a credit bureau, or via private credit score companies. Djankov et al . (2007) present evidence supporting the effectiveness of public credit registries in poor French legal origin countries. Conversely, as highlighted by Babina et al. (2024a , b) and Nam (2023) , significant differences exist between credit registries and open banking: (1) open banking provides a broader spectrum of information; (2) it offers customers the autonomy to choose whether to share their data, along with easier access to their information and (3) it extends the utility of data beyond lending to encompass a wider range of applications. These distinctions amplify the privacy concerns associated with open banking (See, e.g. Brunnermeier and Payne (2022) ), suggesting that privacy protection will pose a novel challenge for governments in the era of open banking (See, e.g. Acquisti et al . (2016) , Abowd and Schmutte (2019) , Jones and Tonetti (2020) and Bian et al . (2021) for general discussion on privacy).

5. Conclusion

As highlighted by Vives (2019) , disruptive technologies such as big data and blockchain have profoundly altered the landscape of the financial system. Fintech companies and other non-bank entities, leveraging these innovative technologies, have not only emerged as formidable competitors but also as potential collaborators with traditional commercial banks. Vives (2019) emphasizes the challenge for regulators in ensuring a level playing field. This paper reviews the burgeoning literature on open banking, a concept aimed at maintaining equality between commercial banks and fintech companies in terms of data access, while also encouraging areas for potential collaboration. As an evolving area of study, open banking research has begun addressing a broad spectrum of foundational questions, including competition, data ownership and government regulation. However, the field continues to confront numerous unresolved challenges.

First, open banking introduces significant challenges to the traditional banking model, particularly in how banks have addressed the free-rider problem by keeping customer information confidential. This strategy has enabled banks to conduct thorough customer screening without sharing the fruits of their labor with competitors. However, the rise of open banking, advocating for more transparent data sharing, might compel banks to either scale back their screening efforts, considering the potential for competitors to benefit from their diligence, or to selectively share information, thus protecting their competitive advantage.

Furthermore, the interplay between open banking and financial risk presents an area ripe for exploration. The redistribution of access to sensitive financial information could have profound implications for risk assessment, fraud prevention and the overall stability of the financial system. Therefore, both theoretical frameworks that address these new dynamics and empirical research that provides evidence from the real-world implementation of open banking are essential for a comprehensive understanding of its impact. Second, while initial empirical studies by Fang and Zhu (2023) , Nam (2023) and Babina et al. (2024a , b) have started to explore the implications of open banking, echoing the theoretical insights of He et al . (2023) , Brunnermeier and Payne (2022) , Goldstein et al . (2022) and Parlour et al . (2022) , there is still much ground to cover in empirical research on open banking. Moving forward, a surge in empirical investigations is expected, focusing on the nuanced economic impacts of open banking, including its effect on market competition, innovation and financial inclusion. Additionally, cross country analyses and policy assessments will be pivotal in distilling best practices and tailoring open banking frameworks to meet diverse regulatory and market needs. This burgeoning area of research promises to shed light on the operational realities of open banking, offering valuable insights.

Lastly, the importance of consumer education in the context of open banking cannot be overstated. As financial ecosystems evolve to become more interconnected and data-driven, consumers stand at the crossroads of innovation and vulnerability. Thus, efforts to enhance consumer awareness and digital literacy will be key to helping individuals make informed decisions about their financial data, ensuring they can use open banking safely. This includes understanding the implications of data sharing, recognizing the potential for privacy breaches and knowing the rights and protections available to them.

See Chesbrough (2003) for case studies about companies’ transformation from closed innovation paradigm to open innovation paradigm such as IBM and Intel. See Chesbrough (2011 , 2019) for examples such as smart cities and smart villages utilizing the idea of open innovation.

See Cevik (2024) for an empirical study on the relationship between fintech and financial stability.

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Further reading

Leong , E. ( 2020 ), “ Open banking: the changing nature of regulating banking data-a case study of Australia and Singapore ”, Banking and Finance Law Review , 35.3 , pp. 443 - 469 .

Nanaeva , Z. , Aysan , A.F. and Shirazi , N.S. ( 2021 ), “ Open banking in Europe: the effect of the revised payment services directive on Solarisbank and Insha ”, Journal of Payments Strategy and Systems , Vol.  15 No.  4 , pp.  432 - 444 .

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Open Access

Peer-reviewed

Research Article

Open banking: A bibliometric analysis-driven definition

Contributed equally to this work with: Gorka Koldobika Briones de Araluze, Natalia Cassinello Plaza

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Universidad Pontificia Comillas, Madrid, Spain

Roles Conceptualization, Funding acquisition, Investigation, Methodology, Supervision, Validation, Writing – original draft, Writing – review & editing

Affiliation Departament of Financial Management, Facultad de Ciencias Económicas y Empresariales, Universidad Pontificia Comillas, Madrid, Spain

• Gorka Koldobika Briones de Araluze, 
• Natalia Cassinello Plaza

• Published: October 3, 2022
• https://doi.org/10.1371/journal.pone.0275496
• Peer Review
• Reader Comments

“Open banking,” as a concept, was initially developed by a UK regulation to foster competition in banking through sharing client data (with their consent) amongst competitors. Today, it is regulated in several most relevant banking jurisdictions. Despite its growing relevance, consensus about the definition of open banking is lacking. This study examines 282 articles on open banking using bibliometric clustering techniques. Moreover, within the 282 articles and applying discourse analysis, we analyze 47 idiosyncratic definitions of open banking to test an integral framework that supports our proposed definition of the concept. Our study contributes to the literature by providing a generalized multidisciplinary definition of open banking. It identifies four main drivers behind the concept: business model change, client data sharing, incorporation of technological companies (fintechs and others), and regulation. These four elements, which should be considered in new regulations in the globalized banking sector, foresee open banking as a critical enabler of a new strategic dynamic in banking.

Citation: Briones de Araluze GK, Cassinello Plaza N (2022) Open banking: A bibliometric analysis-driven definition. PLoS ONE 17(10): e0275496. https://doi.org/10.1371/journal.pone.0275496

Editor: Tawei (David) Wang, DePaul University, UNITED STATES

Received: February 6, 2022; Accepted: September 19, 2022; Published: October 3, 2022

Copyright: © 2022 Briones de Araluze, Cassinello Plaza. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

What is open banking? Since the inception of the “Open Banking Working Group” in the United Kingdom in 2015, open banking has generally been considered as the platformization of the retail banking industry [ 1 , 2 ]. To date, it has spread worldwide from the UK to Continental Europe, America, and Asia, constituting one of the retail banking industry’s shaping forces of the future [ 3 , 4 ]. Thus, on top of the open banking initiative in the UK and PSD2 (Payment Services Directive 2) in the European Union, there are open banking regulations in Australia, India, México, and Brazil, and forthcoming regulations in Russia and Canada.

The essence of open banking regulations is to recognize the banking clients’ right to share their transactional data with authorized third parties and detailed provisions on how to materialize this right [ 5 ]. Despite its apparent simplicity, this data-sharing right constitutes the primary vector for fostering the transformation of the retail banking sector from a closed business model to an open platform, similar to what occurred in telecommunications, power, and gas industries [ 6 ].

Open banking originated from practitioners and was inspired by the open data, open-APIs (Application Programming Interfaces), and open innovation philosophies [ 7 ] applied to the retail banking business [ 8 , 9 ]. The business community is analyzing this phenomenon extensively, understanding it as a “ collaborative model in which banking data is shared through APIs between two or more unaffiliated parties to deliver enhanced capabilities to the marketplace ” [ 10 ].

Its first implementation worldwide materialized in the UK. It was requested by the Competition and Markets Authority as a foundational strategy to ascertain that personal current accounts, as well as small and medium-sized enterprises’ banking markets, serve customers better. This issue emanated from a retail banking market investigation concluded in 2016 [ 8 ]. It also inspired the European Commission to publish the PSD2 [ 7 , 11 , 12 ]. Although open banking is still in its initial stages of development, the concept has been embraced by practitioners and regulators, being regarded as one of the shaping forces of the financial industry worldwide [ 4 ].

Nevertheless, despite existing literature acknowledging the importance of open banking as a critical retail banking industry’s transformational lever [ 13 ], open banking as a research object still lacks conceptualization both theoretically and empirically [ 14 ]. Academic literature on the subject is still in its early stages of development. Out of 990 documents registered in the Google Scholar database (Aug 6, 2021) containing the term “open banking,” only 57 were published in Scopus-rated peer-reviewed academic journals.

Considering its international and multidisciplinary nature, open banking as a research object presents several challenges. To begin with, open banking is being studied in many academic fields, and researchers who represent different disciplines seem not to converge on a shared definition of open banking [ 14 ]. Additionally, most authors researching the topic leverage idiosyncratic definitions aligned with their respective research focus [ 15 ]. Moreover, subtle differences among open banking regulations worldwide create confusion when comparing publications from different geographies [ 3 ]. Hence, our study aims to establish a generalized definition of open banking and its varying interpretations in different disciplines and geographies. A generalized definition of open banking would add consistency and robustness to existing research, laying out a solid foundation to support high-quality research on the phenomenon.

Apart from a generalized definition, understanding different contexts in which the term “open banking” is used is also essential. Open banking can be discussed from different perspectives (regulatory, technological, economic, and managerial) that imply different nuances, which should be identified. Additionally, it is also critical to validate a generalized definition under these different contexts to assure that it works properly in all of them.

This study aims to understand the contexts and meanings of the term “open banking” and proposes a generalized definition that can be used unambiguously in the academic literature. For this objective, two methodologies are used. First, through clustering-based bibliometric analysis, 282 academic articles are analyzed to identify the areas, contexts, or meanings of “open banking.” Second, applying a “discourse analysis” methodology, the 47 definitions of open banking found in the literature are examined, and a generalized definition of the term applicable to all open banking connotations is proposed.

Our study makes several contributions to the literature. First, it performs a review of the pre-existing literature on open banking applying bibliometric techniques. Second, a generalized definition of open banking and its four applications (business model, fintech, data-sharing, and regulation) are proposed. Third, the 47 existing open banking definitions are systematically analyzed, and a classification is proposed for them (institutional, ecosystem, and client). Likewise, generated inductively, an “open banking integrated definition framework” is formulated based on eight elements that can be applied to similar definitions. Finally, the Hirschman Herfindahl Index (HHI) is used innovatively within the discourse analysis to measure the degree of consensus regarding the definition.

2. Literature review and research question

Open banking is a new phenomenon in the banking industry and an even newer concept in academia. Before 2016, only four articles contained the term “open banking” in academic or grey journals. Hence, open banking can be considered a new study object.

Existing literature can be grouped into three blocks: regulatory, technical, and managerial. The regulatory literature analyzes the legislation that supports open banking (European Union’s Second Payment Services Directive [PSD2], UK’s Open Banking Standard, Australia’s Consumer Data Right, Singapore’s Personal Data Protection Act, India’s Aadhaar and Unified Payments Interface, and similar regulatory pieces being analyzed and approved in Hong Kong, Canada, Brazil, [BCB Circular No. 4,015/2020], and Mexico (Ley Fintech). Existing publications either focus on a single jurisdiction [ 16 – 19 ] or compare different legislations [ 20 , 21 ]. From a technology perspective, existing literature focuses on the underlying infrastructure [ 22 – 24 ] as well as on the acceptance of the open banking technology from the customer’s perspective [ 25 – 27 ]. Managerial literature analyzes structural changes in the demand and supply of financial services in the retail banking market due to open banking [ 7 , 10 , 12 , 28 – 30 ]. Finally, other fields such as microeconomics are also starting to analyze the phenomenon [ 31 ].

Nevertheless, despite a growing academic interest in open banking, foundational literature is still missing. There are no publications analyzing the origins of open banking (why open banking is needed), the nature of the phenomenon (how open banking has developed in different geographies) or, even more basic, what open banking is. As a matter of fact, there are only three publications devoted to establishing a definition of open banking. van Zeeland and Pierson (2021) follow a bibliometric and discourse analysis approach for open banking, but they fail to propose a definition, concluding that:

“Open Banking could be all kinds of things , from a remedy to an ecosystem , or most often : a (business) model of some sort . Its purposes are considered to be providing new (‘better’ , ‘customer-centric’) services to customers and improving competition in the banking market by letting ‘third parties’ in . ” [ 14 ]

O’Leary et al. (2021), building on an open data lenses approach, propose the following definition:

“An initiative which facilitates the secure sharing of account data with licensed third parties through Application Programming Interfaces (APIs) , empowering customers with ownership of their own data . The initiative aims to increase competition in retail banking by developing innovative products and services which will bring increased value to customers . ” [ 15 ]

Finally, Laplante and Kshetri (2021) approach the need for a definition of open banking, but do not provide a generalized definition other than describing the phenomenon as:

“Open banking describes a special kind of financial ecosystem . The ecosystem provides third-party financial service providers open access to consumer banking , transaction , and other financial data from banks and nonbank financial institutions through the use of application programming interfaces (APIs) . ” [ 32 ]

The existing definitions of open banking present three types of problems fundamentally: perspective bias, discipline bias, and purpose bias. Starting with the perspective bias problem, open banking is a tripartite scheme between the owner of the data, custodian, and third party who accesses it. Any general definition must consider the three agents to avoid partial or incomplete analysis of the phenomenon. Regarding the discipline bias problem, researchers tend to confuse the context in which open banking is used in their discipline with a generally applicable definition. Thus, technical literature focuses exclusively on the technological support of the phenomenon, the regulatory literature on its legal support, and the management literature on the possible implications for the business model. However, a generalized concept of open banking must be able to encompass all its contexts of use and not just one of the meanings. Finally, the purpose bias problem consists of giving open banking a specific purpose other than the one for which it was formulated: to increase competition in retail banking by facilitating the entry of new competitors. Considering the combined effect of the three biases, the definitions proposed so far of open banking do not allow the construction of solid and generalizable knowledge about the phenomenon, which is a significant caveat on its development.

One last question is why academic research on open banking is relevant. There are no global figures for the investment required to materialize open banking. According to Tink, one of the world’s leading open banking service providers [ 33 ], the average open banking expenditure for a retail bank in Europe in 2020 was €83.1 Mn. So, the aggregated figure for the system should be in the range of tenths of billions annually, just for Europe. Nevertheless, we have no evidence, based on scientific studies, of the intention of customers to use services based on open banking. There is no scientific evidence on how open banking can impact value creation and distribution in retail banking. No robust academic studies explain the conditions under which customers are willing to share data with third-party providers. In short, the academia has dealt with accessory elements of open banking but not with the central aspects of the phenomenon. The lack of a robust and generally shared definition of the phenomenon allowing collaboration among researchers and a holistic view of the phenomenon, is at the heart of this knowledge gap.

Thus, a generalized definition of open banking together with a detailed understanding of different contexts in which the “open banking” concept is used is a relevant gap in the academic literature that needs to be filled. A particular contribution of this study is that it tackles the research question through a multidisciplinary approach, integrating views from different knowledge domains and through mixed quantitative-qualitative techniques, specifically bibliometric research and discourse analysis.

3. Methodology

This study follows a three-tiered approach to present a potential generalized definition of open banking ( Fig 1 ). First, using bibliometric techniques, we map existing literature (282 documents) and, by applying co-word analysis, cluster co-occurring terms to identify conceptual domains related to open banking. The clustering analysis is executed using Visualization of Similarities (VoS), an evolution of Multidimensional Scaling (MDS) algorithms. From this analysis, we identify four clusters that inform the existing open banking literature and examine the interaction among them. Second, by applying a discourse analysis approach, we analyze existing definitions of open banking in the literature (47 definitions found in the 282 articles) to reveal critical attributes mentioned in these definitions considering their disciplinary and geographical variations. We, then, profile the descriptors used concerning each attribute and propose a framework to analyze existing open banking definitions. Third, based on the analysis, we outline an integrative definition of open banking, identify limitations of the investigation, and propose future research developments.

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https://doi.org/10.1371/journal.pone.0275496.g001

The analysis supporting this publication combines two methodological approaches: bibliometric and discourse analysis. First, we identify and analyze all relevant open banking literature and cluster the main perspectives on the topic by leveraging bibliometric techniques. Then, we extract 47 idiosyncratic, partial, or working definitions of open banking identified in the dataset. Applying critical discourse analysis, a method that has been accepted in the academic literature as a valid procedure for social sciences research [ 32 , 34 ], we systematically examine the 47 definitions to deduce a general definition for open banking and interpret the results.

3.1. Bibliometric analysis

3.1.1. analytical approach..

Bibliometrics refers to the field that investigates groups of publications applying quantitative analysis methods [ 35 ]. Although this technique was initiated during the 1950–1960 period, it gained traction in the last two decades with the emergence of large electronic databases of academic articles, such as Web of Science (WoS) and Scopus, and the generalization of bibliometric analytics software packages, such as Gephi, Leximancer, and VOSviewer [ 36 ].

Bibliometric analysis techniques can be divided into three prominent families according to their goal [ 37 ]: techniques for establishing a relationship between authors (co-author analysis), techniques that aim at establishing a relationship between publications (citation analysis, co-citation analysis, and bibliographic coupling), and techniques for defining relationships within the content of selected publications (co-word analysis). Considering the relative novelty of the topic under consideration and the lack of consolidation of the academic sources considered, this study focuses on co-word analysis to identify the underlying constructs of the open banking concept.

From an analytical point of view, core techniques of bibliometric analysis can be divided into performance analysis and science mapping [ 37 ]. As an evolution of science mapping core techniques, enrichment techniques allow outcome augmentation to produce more advanced insights. This study applies clustering and visualization, both enrichment techniques, to perform a co-word analysis on the dataset that comprises all relevant open banking academic literature. Co-word analysis clustering and visualization techniques’ output is a network of topics and their associations, which represent the conceptual domain of a research field. Although clustering and visualization techniques are conceptually different, they usually go hand in hand [ 37 ]. In this study, they are applied simultaneously to analyze the dataset.

3.1.2. Dataset building and process.

Although the first open banking regulation was approved in 2017 in the UK, the concept’s origins are uncertain. Simon Redfern founded the Open Bank Project in 2012 [ 38 ]. But even before that, academic articles have been containing references to “open finance” and “financial aggregation” since 2002 [ 39 ]. Consequently, our database includes articles about “open banking” since 2002.

The initial dataset consists of 990 documents identified through a search in the Google Scholar database for articles using “open banking” as a keyword, conducted on August 6, 2021. The search is carried out through the Publish or Perish software tool.

Since its launch in 2004, Google Scholar has positioned itself as the most comprehensive academic citations database compared with alternative options such as WoS or Scopus, especially for humanities and social sciences [ 40 ]. However, Google Scholar contains articles not published in peer-reviewed journals, which requires additional filtering to ensure the quality of the database. Thus, Publish or Perish is commonly used in bibliometric analysis to filter academic publications databases [ 40 ].

Only documents written in English are selected due to the clustering analysis’ language requirements (663 articles). Two filters are subsequently applied: documents containing “open banking” in the title (92 papers) and records that contained “open banking” in the abstract and that had at least one citation (264 documents), obtaining 356 articles. To include articles with at least one citation is a potential quality filter of literature referenced in Google Scholar and is consistent with academic procedures [ 41 , 42 ] and recent bibliometric publications on the topic [ 14 ]. An additional check is performed to ensure that all the articles referenced in Scopus and WoS related to the topic are contained in the filtered database. After that, the remaining papers are fully read with two objectives. First, on the bibliometric side, to reject false positives of the combination of the words “open” and “banking,” obtaining the final list of 282 documents from 2002 to 2021 ( Fig 2 ). The resulting dataset is uploaded to RefWorks, a commonly used reference manager software [ 26 ]. Second, on the content analysis approach, to extract all the definitions of “open banking” included in the dataset. Forty-eight definitions of “open banking,” transcribed in Tables 3–5 of S1 Annex , are identified and recorded in an excel database ( S1 Annex ) [ 42 ].

https://doi.org/10.1371/journal.pone.0275496.g002

Due to limitations in obtaining full-text searchable versions of all the articles in the dataset, co-wording analysis is performed only on the titles and abstracts. This approach is consistent with existing bibliometric techniques as described in the literature [ 36 ]. These 282 articles yield 5,000 terms; out of which only those with five or more occurrences are selected (377). Ten generic terms (article, case, case study, chapter, example, interview, number, paper, study, and year) are removed from the selection, finishing with 367 terms. These terms are clustered, defining a minimum size of 25 items per cluster to avoid micro fragmentation of clusters. This process results in four clusters discussed in the results section. The normalization method applied is Linear / Logarithmic, and the proposed visualization layer is built using an attraction parameter of 3 and a rejection parameter of 0. The minimum cluster size is set at 25 [ 43 ], and the iterations number is set at 50.

3.2. Discourse analysis

During the bibliometric analysis dataset-building process, 47 definitions of “open banking” are identified. Each one of them appears in just one article. Although only three articles [ 14 , 15 , 32 ] are devoted to defining open banking, most articles dealing with the topic leveraged idiosyncratic or working definitions. The definitions are extracted and systematically analyzed from two perspectives.

First, a semantic approach is used to understand the role of each definition component. Eight semantic/grammatical elements are identified by applying an inductive approach: Nature, Consent, Subject, Action, Object, Recipient, Process, and Purpose. These eight elements constitute our proposal of an “open banking integrated definition framework,” which is discussed in detail in the Results section.

Second, to test the framework’s robustness, a descriptive statistics approach is applied to understand (i) the degree of completion of the definitions identified according to the proposed framework and (ii) the level of convergence/dispersion in the definitions. HHI is applied to the definitions to assess the convergence/dispersion within each element.

In our case, we calculate HHI for each conceptual field identified in the definitions. For each of the eight elements, if the 47 definitions used the same concept, HHI would yield a 10,000 (maximum value). If different concepts were used by the 47 definitions, HHI would be 212.8 [47 x (100/47) 2 ].

4.1. Bibliometric analysis and main research trends

As previously mentioned, open banking is a relatively new term in academic literature. The first time it appeared in academic literature fully aligned with the current interpretation was in 2009, but it started to take-off after 2016. The data for 2020 and 2021 ( Fig 2 ) might be affected by the criteria of choosing auxiliary publications that were cited at least once.

Regarding the nature of the documents, the dataset is highly heterogeneous: 20.2% documents [ 57 ] are articles published in Scopus rated journals; 5.0% [ 14 ] are Scopus-listed conference proceedings, and the remaining 211 are primarily reports, books or book sections, and academic dissertations ( Fig 3 ).

https://doi.org/10.1371/journal.pone.0275496.g003

It is worth noting that despite the limited academic relevance of existing literature, it is evolving toward more journal publications and Scopus-listed conference proceedings, implying higher relevance within the academic community ( Fig 4 ).

https://doi.org/10.1371/journal.pone.0275496.g004

Although the main field of study for open banking, following Scopus classification, is Business , Management , and Accounting , interest in the phenomenon is growing in other disciplines, too. In fact, in 2020, Business , Management , and Accounting accounted for 30.2% of the documents published, Computer Sciences accounted for 27.1%, Social Science–Law accounted for 14.6%, Economics , Econometrics , and Finance accounted for 11.5%, and other fields ( Medicine , Engineering , Social Science–Other ) accounted for 16.7% ( Fig 5 ).

https://doi.org/10.1371/journal.pone.0275496.g005

Observation 1.1. While the interest of academia in the open banking phenomenon is still limited, it is growing significantly over the last few years.

Observation 1.2 The quality of academic literature analyzing open banking is increasing, with a higher number and proportion of publications in higher-rated magazines.

Observation 1.3 Open banking is a multidisciplinary phenomenon that is being studied by several disciplines.

4.2. Clustering analysis and main conceptual domains (drivers) of open banking

Through the application of the VoS algorithm, four clusters are identified ( Fig 6 ). These clusters are groups of keywords that appear in at least five documents. Table 1 summarizes the top 10 keywords for each cluster.

https://doi.org/10.1371/journal.pone.0275496.g006

https://doi.org/10.1371/journal.pone.0275496.t001

Before coding, both researchers agreed on the coding method: based on heuristics, assigning to the cluster a description that explained at least 50% terms included in each cluster. Both researchers performed independent coding, and the results were compared and discussed to obtain the proposed interpretation.

Cluster 1 ( Business model platformization ): the initial list included both “bank” and “banking,” and both terms were consolidated. Here, open banking could be interpreted as the transformation process of the retail banking business model toward a platform leveraging API technology and fostering innovation.

Cluster 2 ( Data sharing ): summarizes the main open banking features: a new framework involving data (information) sharing and opening the banking market to competition, which poses new challenges and risks for legacy players.

Cluster 3 ( Fintech ): summarizes the ecosystem impact of the fintech phenomenon as a new competitor for financial institutions. From the initial outcome of the analysis, several generic keywords were removed for interpretation purposes: “research,” “impact,” “use,” “level,” “role,” “factor,” and “effect.” Additionally, “service” was consolidated with “financial services” for clarity.

Cluster 4 ( Regulation ): reflects the regulatory side, focusing on the legal and jurisdictional implications.

Observation 2.1.

Open banking as a research field is built on four domains: business model platformization, data sharing, fintech, and regulation, all of which can be interpreted as different connotations of open banking.

Observation 2.2.

Each identified cluster has a strong relationship with different knowledge domains.

Observation 2.3.

Clustering analysis confirms the adequacy of a multidisciplinary approach, considering the heterogeneous nature of the phenomenon and the associated literature.

4.3. Analysis of open banking definitions

Next, the final 282-document dataset was manually read, searching for formal or idiosyncratic definitions of open banking, the result of which is 47 definitions ( S1 Annex ; Tables 3–5)

Existing literature does not provide a framework to analyze “open banking” or similar definitions. Following similar approaches in the academic literature [ 45 , 46 ], the authors proceed to build an ad-hoc framework: the “open banking integrated definition framework” based on induction from the 47 existing definitions. This process identifies eight elements in which all current definitions can be decomposed.

The definitions are then decomposed into eight elements categorized into the following three blocks and analyzed to deduce a general definition of open banking constituting the “open banking integrated definition framework”: (i) Conceptual elements: Nature ( How can the phenomenon be classified ?) and Consent ( What is the enforceability ?), (ii) Core attributes: Subject: ( Who is the actor ?); Action ( What is expected from the Subject ?); Object ( What is the target of the Action ); Recipient ( Who is affected by the Action ?) and Process ( How does the Subject interact with the Object and with the Recipient ?), (iii) Purpose ( What is the final goal ?).

After applying the proposed framework to the 47 definitions, we find that 79% contain five or more elements of the definitions ( Fig 7 ), which implies significant robustness of the proposed framework.

https://doi.org/10.1371/journal.pone.0275496.g007

Table 2 shows the three primary outcomes for each element and the percentage of definitions containing the term. Not surprisingly, the level of consensus calculated through the HHI varies significantly across concepts. Additionally, for each element, the table contains the percentage of definitions that contain the element.

https://doi.org/10.1371/journal.pone.0275496.t002

Starting with the conceptual elements, there are two different perspectives: the regulatory approach , where open banking is understood as a legal construct, and the framework approach , which focuses on the interactions between players, regardless of the regulation. This duality is compatible with the fact that there are specific open banking regulations in some geographical areas (UK, Europe, and Australia). In contrast, in other regions (US and Canada), open banking exists as a phenomenon but without a specific regulation in place yet. We find a tight relationship between Nature and Consent , considering that regulation implies requirement, obligation, or empowerment, while framework implies enablement.

Regarding core attributes, the main keywords are “sharing” for Action and “APIs” for Process . Nevertheless, the interpretation of both should be significantly different. Regarding Action , there is a high consensus among all definitions around “sharing,” which is consubstantial with the very notion of open banking as currently understood by practitioners [ 47 ]. However, talking about Process , although currently, APIs are the most common system interface technology, the open banking phenomenon could be perfectly conceived by leveraging different interface technologies such as screen scraping [ 48 ]. That is why API should be deemed a relevant yet not essential element in the definition of open banking.

As for Subject , there is a low degree of consensus: 30.6% definitions are built around “ customer ,” 25.0% around “ banks ” (including synonyms such as “ financial institutions ”), and 19.4% around “ third parties .” This lack of convergence emerges from the fact that open banking can be formulated under three perspectives: the client perspective: “ customers–share ,” institutional perspective: “ banks–make available ,” and ecosystem perspective: “ third parties–access .” However, it is still unclear which approach is better. Nevertheless, the fact is that comparing roles of the three main actors in the open banking process, banks are passive agents, and their only function is to facilitate access to data. Similarly, third parties such as fintechs, for that matter any third party, cannot force a customer to enter into an open banking relationship with a banking client. That is why the client perspective seems crucial to understanding the essence of open banking as a “right to share” rather than a “right to access.”

The Object of open banking is also unclear, ranging from “ data” to “ applications and services . ” Lastly, concerning the Recipient , there are different levels of concretion, from a general conception (“ third parties ”) to specific type players ( “fintechs” ). There is, however, one open matter, “payments initiation.” Apart from data sharing, some regulations also include payment initiation as an object of open banking (e.g., UK, EU, India, and Brazil). However, there are minimal academic literature references to this matter. Thus, we will attach to the mainstream definitions of open banking as data sharing.

Finally, the Purpose element is highly undefined. Although “transparency” and “ competition” appear in several cases, there is no convergence in the final goal of open banking in any of the analyzed definitions.

In sum, although consensus around different elements of open banking is limited, it could be defined as “a generally regulated framework that enables banking customers to share their data with third parties, commonly through standardized interfaces such as APIs, to increase competition in the financial sector.” The proposed definition covers the eight elements identified in the proposed open banking integrated framework and could be understood as a generalization of all the analyzed partial definitions.

Observation 3.1.

There is neither a single definition of open banking in the academic literature nor a specific definition by knowledge domain. Instead, there is a collection of idiosyncratic and paper-specific approaches toward its definition.

Observation 3.2.

Among existing definitions, there are strong commonalities in some elements, while others show a high degree of dispersion. These differences arise mainly from different knowledge domains through which open banking is analyzed and various jurisdictions where it occurs.

Observation 3.3.

Despite underlying divergences, a standard definition of open banking can be formulated and leveraged in all conceptual domains based on the proposed approach.

Observation 3.4.

Despite customers playing a central role in different definitions of open banking as the owner of data, decision-maker of data sharing, and target of the framework’s purpose, one key element where prior research lacks consensus and focus is the role of a banking customer within open banking. Only 30.6% definitions are built around the word “client” (compared with 25.0% definitions that are built around “banks” and 19.4% around “third parties”).

5. Discussion and conclusions

Our bibliometric analysis confirms the academic community’s limited but growing interest in open banking and the challenges of a multidisciplinary approach to the phenomenon. Together with the intrinsic fragmentation in the analysis of the phenomenon due to its regulatory facets, both elements result in a corpus of literature that is still getting consolidated but lacks some foundations for further development.

Based on the clustering analysis’ results of the nascent literature, four conceptual clusters have been identified. These are (i) the platformization of the retail banking industry business model; (ii) a manifestation of the overall data sharing trend applied to the banking data; (iii) the interaction between the emergent fintech ecosystem and incumbent financial institutions; and (iv) the regulatory framework that, in some jurisdictions, bolsters the open banking phenomenon. These four clusters can be interpreted as different connotations underpinning the concept of “open banking.” Hence, the complex nature of open banking is a considerable challenge for future literature development, as partial analysis of the phenomenon will yield limited conclusions. Thus, only multidisciplinary approaches will offer good insights.

A clustering analysis to identify the conceptual domains around the open banking definition is also a valuable contribution. As an unsupervised learning methodology, clustering analysis returns an objective output, eliminating pre-classification biases. Moreover, the clustering approach unveils all the critical factors behind the open banking concept, supporting our proposal of an integrative definition valid across all disciplines and realizations of open banking. Consequently, although there are strong linkages between Cluster 1 (Business model/Platform), Cluster 4 (Regulation), and the academic literature emanating from Business Management and Social Sciences-Law, respectively, Cluster 2 (Data sharing) and Cluster 3 (Fintech) unveil purely transversal conceptual domains, multidisciplinary in nature that do not match with a single academic field and that could not have been identified without the clustering approach.

The detailed analysis of the 47 identified idiosyncratic and working definitions of the phenomenon confirms the need for a generalized conceptualization that amalgamates all existing perspectives on the topic. The proposed framework arising from the definition analysis is by itself a valuable tool for understanding the depth of open banking and the importance of identifying all relevant components that intervene in its dynamics. It is also important to note that the different formulations for the Subject of open banking constitute three perspectives of the phenomenon. These include (i) the “institutional perspective,” which analyzes open banking based on the obligations to comply with banking regulation; (ii) the “ecosystem perspective,” which focuses on the potential mechanics and benefits for new entrants, especially fintechs, from accessing banking clients’ data; and (iii) the “client perspective,” which studies the fundamental data-sharing right that constitutes the basis of open banking. Although the literature has not been explicit on this matter, researchers need to understand the implications of each positioning.

This study contributes to filling the literature gap with a potential generalized multidisciplinary open banking definition. Our proposed definition encompasses the four conceptual domains identified through the cluster analysis of the existing literature. Further, our proposed definition contributes to synthesizing different approaches, serving as a catalyzer for further research on the topic and significantly enhancing multidisciplinary approaches to the question.

Our proposed generalized definition should help increase collaboration among researchers from different academic disciplines and cooperation among researchers in different geographies to analyze the open banking phenomenon. Additionally, the proposed definition is especially relevant for policymakers and private economic agents, considering current ongoing discussions around the evolution of open banking regulation. Finally, the generalization of the open banking concept is also relevant for end customers as data owners and primary beneficiaries of open banking regulations.

The main limitation of this analysis is the emergent nature of the existing literature. Although several quality filters have been applied to the inputs to ensure the quality of the outcomes, this approach could be replicated in the future on articles published in peer-reviewed journals once a sufficient corpus of high-quality literature has been developed.

Supporting information

S1 annex. open banking definitions [ 2 , 5 , 9 , 13 – 17 , 19 , 24 , 26 , 31 , 32 , 48 – 81 ]..

https://doi.org/10.1371/journal.pone.0275496.s001

S2 Annex. Analytical approach [ 82 – 84 ].

https://doi.org/10.1371/journal.pone.0275496.s002

S1 File. Cluster map.

https://doi.org/10.1371/journal.pone.0275496.s003

S2 File. Cluster network.

https://doi.org/10.1371/journal.pone.0275496.s004

S3 File. Terms thesaurus.

https://doi.org/10.1371/journal.pone.0275496.s005

https://doi.org/10.1371/journal.pone.0275496.s006

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