Regulating Innovation: The Role of Law in Shaping Business Transformation

1. Introduction

1.1 Background and Problem Statement

The present-day business change has become more and more characterised by innovation. The emergence of digital technology, data analytics, artificial intelligence, and platform-based business models has significantly changed how companies generate their value, engage with their consumers, and compete in the global market. No other field is more subject to this transformation than financial technology (FinTech) and AI-enabled business services, where algorithm decision-making, digital platforms, and automated operations have changed the way services have been delivered historically (Bessen, 2018; Zetzsche, Buckley, Arner, & Barberis, 2017a).

The pace and magnitude of innovation have, however, revealed an ongoing conflict between legal regulation and technological change. Laws and regulatory frameworks traditionally created for more stable economic environments, are usually not able to keep up with fast changing technologies. This regulatory delay provides businesses with uncertainty in terms of business innovation, and it also poses a question to regulators when it comes to consumer protection, integrity of the market, and protection of basic rights (Brownsword, 2019; Fenwick, Kaal & Vermeulen, 2017).

In FinTech, such as in the case of digital payment systems, peer-to-peer lending and crypto-assets, current financial regulatory frameworks are being challenged by new financial regulatory structures structured around more traditional banks. In the same regard, AI-enabled services pose intricate legal issues of data privacy, transparency of the algorithms, responsibility, discrimination, and responsibility. Innovation in both industries does not simply bring new products, it is a process of altering the way organisations are governed, the management of risks, compliance frameworks, and strategic decision-making.

It is against this backdrop that law has got an ambivalent role to play. On the one hand, strict or obsolete regulation can prevent experimentation and discourage investment. Conversely, poor or weak legal systems can erode trust, put consumers in harm ways and dislodge markets. This article will start by assuming that law is not only a responsive limitation to innovation, but also a shaping influence which determines the direction, excellence, and sustainability of business change.

1.2 Aim, Objectives, and Research Questions

The main objective of the article is to explore the role of legal and regulatory frameworks in innovation-based business transformation, especially FinTech and AI-enabled service industries.

The main objectives include:

1.     Discuss how law can suppress or foster innovation-intensive business.

2.     Examine regulatory tools of FinTech and AI-based markets to control uncertainty and risk.

3.     Investigate the interplay between fundamental areas of law, such as data protection, financial regulation, competition law, intellectual property and corporate governance and business transformation.

4. Propose legal and regulation design principles that would promote responsible innovation without unduly restraining business dynamism.

The research questions addressed in the article are:

·       In what ways will law ensure the future of innovation-driven business change in FinTech and AI-enabled services?

• What regulation methods will maximize the integrity of the market, consumer protection and innovation?

• What can businesses do to make legal compliance a part of their transformation strategies to be a source of resilience and competitive advantage?

1.3 Scope and Significance of the Study

The article adopts a sector-focused approach, concentrating on FinTech and AI-enabled business services due to their high levels of regulatory complexity, societal impact, and economic significance. Although the discussion will rely on the global regulatory developments, the analysis will not be limited to one jurisdiction. Rather it outlines general patterns and principles of regulation that cut across legal systems.

The significance of this article is in the integrative perspective of the study. Much of the existing literature treats innovation regulation, business strategy, and legal compliance as separate domains. This article brings these strands together by demonstrating how legal frameworks actively structure business transformation processes. For regulators, the study highlights the importance of adaptive and risk-sensitive regulatory design. In the case of businesses, it highlights the importance of viewing law as a strategic element of innovation, and not a compliance burden that comes after an innovation.

1.4 Structure of the Article

After this introduction, Section 2 conducts a literature review on the available literature on the topics of innovation, regulation, and transformation of business with a focus on FinTech and AI-based services. Section 3 elaborates the theoretical and conceptual framework that is used to develop the analysis. Section 4 discusses the methodology used and the justification for it. The findings of the study are provided and discussed in section 5. Section 6 provides policy and strategic recommendations whereas Section 7 is a conclusion to the article.

2. Literature Review

2.1 Innovation and Regulation: Threat or Necessary?

The recent history of scholarly discussion on innovation regulation has long swung along between two rival storeys. The former participants of the debate depict regulation as an obstacle to innovation because, according to them, too much legal controls increase costs, experimentation slows down, and puts off entrepreneurial undertaking. Such a perspective is frequently linked to the notion of “permissionless innovation”, which suggests minimal regulatory interference, especially on digital markets (Thierer, 2016).

Conversely, there is an accumulating literature on why regulation is not necessarily hostile to innovation. Empirical and theoretical research is increasingly indicating that carefully crafted law can be used to facilitate innovation via less uncertainty, reduced investment risk, and consumer trust (Blind, Petersen, & Riillo, 2017; Porter & van der Linde, 1995). Regulatory predictability concerning the field of FinTech through licensing, consumer protection, and prudential standards has been reported to facilitate market entry and growth by legitimate businesses (Arner, Barberis, and Buckley, 2017).

In the context of AI-enabled services, researchers highlight that legal protection to address the issue of data protection, transparency, and accountability is fundamental to maintaining the trust of the population in algorithms. Without such safeguards, innovation risks social backlash, reputational harm, and regulatory intervention that may ultimately be more restrictive (Floridi et al., 2018).

2.2 Business Transformation in FinTech and AI-Enabled Services

The literature on business transformation notes that innovation is manifesting more and more in products and services, as well as organisational structures, governance arrangements, and value chains. FinTech companies are often exist as hybrids of both technological and financial intermediary companies, which requires new compliance models and risk management practices (Zetzsche et al., 2017a).

Equally, AI-driven business services are pegged on predictive analytics, automation, and data-driven decisions. Such technologies transform labour relations, relationships with clients, and control mechanisms and pose legal challenges regarding explainability, bias, and responsibility (Wachter, Mittelstadt, and Floridi, 2017). According to scholars, a legal compliance is no longer an issue confined to business strategy, but is now part of system design, corporate governance, and operational processes (Cath, 2018).

The literature acknowledges that firms that are able to integrate legal requirements within their innovation models are better placed to expand sustainably and survive regulatory emanations.

2.3 Managing Innovation Risk: Regulatory Tools

Some scholars have recently been concerned with the rise of adaptive regulatory instruments aimed at managing technological uncertainty. FinTech Regulatory sandboxes in specific have gained a lot of coverage in FinTech literature. Sandboxes enable companies to pilot innovative products in the regulated environment, minimising uncertainty without harming the consumers (Jenik & Lauer, 2017; Zetzsche, Buckley, Arner & Barberis, 2017b).

In addition to sandboxes, researchers pinpoint the increased application of principles-based regulation, soft law tools, and regulatory guidance to AI regulation. These tools are flexible and allow regulators to act in an iterative manner in relation to technological developments (Fenwick et al., 2017). Critics, in turn, caution that when overly relying on soft law, accountability can be undermined and uneven enforcement can emerge (Brownsword, 2019).

2.4 Gaps in the Existing Literature

Although there is a great deal of scholarly work on innovation regulation, there are still significant gaps. First, most studies discuss FinTech or AI regulation separately, without investigating the interaction of various legal domains to influence the transformation of business. Second, the extent to which firms operationalise regulatory compliance as an element of their transformation strategies is given minimal attention. This article addresses these gaps by offering the full legal discourse on the subject of innovation governance of FinTech services and AI-enabled services.

3. Theoretical and Conceptual Framework

3.1 Theoretical Foundations

Understanding the relationship between law, innovation, and business transformation requires an analytical lens that captures both regulatory behaviour and firm-level adaptation. This article draws primarily on risk-based regulatory theory and dynamic capabilities theory, which together provide a robust framework for explaining how law shapes innovation trajectories in FinTech and AI-enabled business services.

Risk-based regulation has gained prominence as regulators confront technologies characterised by uncertainty, complexity, and asymmetric information. Risk-based regulation does not impose rigid, technology-related rules; rather, it focuses regulatory attention in relation to the magnitude and likelihood of damage an activity could cause (Black and Baldwin, 2010). This is effective in innovative industries where the regulator can differentiate between low-risk testing and high-risk commercial execution, and hence, the marketplace of innovation is preserved without compromising the interests of the people.  Researchers believe that risk-based models are especially appropriate to FinTech and AI regulation since they can balance the intensity of regulation with changes in the intensity of the technological and societal risks (Baldwin, Cave & Lodge, 2011).

In tandem with this view of regulation is the dynamic capabilities theory, which is a project of strategic management literature. Dynamic capabilities are defined as how a firm should be able to feel opportunities and threats, get opportunities and reorganise resources as a reaction to the change in the environment (Teece, 2018). When it comes to innovation regulation, legal compliance is not a fixed requirement but an ever-evolving ability firms should constantly seek to achieve. Better placed to scale and compete in a sustainable manner are FinTech firms and AI-based service providers that are able to predict regulatory expectations, design their systems with compliance in mind and align their governance structures to reflect these expectations (Cath, 2018).

These theories combined, help to elucidate a mutual dependence in that regulators create flexible and risk sensitive legal frameworks and firms create adaptive capabilities to make their way through and influence regulatory environments. In this meaning, law becomes a part of business change and not an external limitation.

3.2 Law as a Structuring Force for Innovation Ecosystems

Along with theory, the practical functioning of law in the ecosystem of innovation can be cognized in the context of its structuring functions. In FinTech and AI based services, the legal regulations do not react to the post hoc innovation; instead, they construct markets, shape organisational structure and shape competition.

First, law can operate as an incentive mechanism. The rules on intellectual property, access to data and the rules of financial licencing affect the location of investment in firms, commercialisation of innovation and which business models are feasible. As an illustration, the efficacy of software copyright, patents, and trade secrets influences the readiness of firms to invest in AI creation, whereas data protection regulations influence the architecture of the data-driven services (Cohen, 2019).

Second, law is used as a type of constraint by establishing limits on what is acceptable behaviour. The compliance costs of consumer protection laws, anti-money laundering requirements and algorithmic accountability requirements also decrease the systemic risk. Prudential regulation where risk-taking is capped in FinTech and non-discrimination and transparency established in AI services restrict the use of opaque decision-making systems (Wachter et al., 2017).

Third, law plays a coordination and trust-building role. Predictable regulatory frameworks reduce uncertainty for investors, partners, and consumers. In sectors where trust is essential—such as digital finance and automated decision-making—legal assurances regarding data security, fairness, and accountability are critical to market acceptance (Bessen, 2018).

3.3 Conceptual Framework: The Regulatory Stack for Business Transformation

Building on the above, this article proposes a conceptual model described as the regulatory stack for innovation-led business transformation. The model describes the process through which a series of layers of law act together to transform business in the FinTech and AI-enabled services.

The first layer is composed of basic legal infrastructure, such as the law of contracts, corporate law, and simple licensing frameworks. These give the legal personality, governance structures and certainty of transactions that any business activity needs.

Innovation-specific regulation is the second level, comprising financial regulation of digital payment systems, data protection laws of processing of personal data, and AI governance systems that deal with algorithmic risk. The regulations directly affect the design and implementation of innovative products and services.

The third level is the market-shaping regulation, especially competition law and platform regulation. In FinTech and AI-driven markets with network effects and concentration of data, competition regulation defines the extent to which the innovation can be challenged or rather controlled by a few powerful players (Khan, 2017).

Adaptive regulatory tools, such as regulatory sandboxes, supervisory guidance and standards, form the last layer. These tools facilitate continuous communication between regulators and innovators, that is, they allow legal frameworks to change with technological innovation (Zetzsche et al., 2017b).

It is at this intersection that business transformation takes place. Companies that identify this regulatory layer and implement their innovation strategies in line with it are more prone to attaining sustainable growth.

4. Methodology: Doctrinal Method

4.1 Nature of the Doctrinal Approach

This methodology used for the study is the doctrinal research, which is traditionally related to the study of law and is concerned with systematic investigation of laws, principles, and legal institutions. Doctrinal research aims to elucidate the nature of the law, its organisation, and functioning in practise, with the main references made to the sources of law, including legislations, the decisions of the court, regulatory tools, and policy documents (Hutchinson and Duncan, 2012).

The doctrinal approach is especially suitable in terms of this article as the research questions are related to the design, functionality, and dynamics of legal frameworks that regulate innovation. Instead of considering empirical results, the research investigates the role of law in determining the circumstances within which business transformation takes place in the FinTech and AI-enabled service industry.

4.2 Sources and Scope of Analysis

The discussion is based on a broad spectrum of primary and secondary legal resources. Financial regulations, data protection statutes, competition law principles, regulatory guidance and policy frameworks on digital innovation are all considered primary sources. Through these documents, regulatory goals, regulatory requirements and regulatory mechanisms such as sandboxes and principles-based regulations are determined.

The secondary sources used in this work are peer-reviewed journal articles, academic books, and authoritative reports published between 2015 and 2026. Such sources offer doctrinal explanation, theoretical clarity and critical analysis of innovation regulation. Earlier theoretical publications are cited where needed to provide basis for the conceptual framework.

The specificity of FinTech and AI-enabled business services provides analytical depth and the possibility of generalisation of regulatory principles that can be applied to other innovation-intensive industries.

4.3 Analytical Strategy

The doctrinal analysis is carried out in three phases. To begin with, the relevant legal frameworks are characterized and categorized according to their regulatory objectives such as reduction of risk, consumer protection, market integrity, and the promotion of innovation. Second, the interface of these structures is investigated to realise the effects of overlapping spheres of law, working together to bring about transformation of business. Third, the results are generalised to derive normative knowledge and design guidelines to govern innovation effectively.

Such an interpretive and integrative approach allows the study to transcend the sphere of descriptive analysis, and to provide evaluative conclusions as to the role of law in the targeted business transformation via innovation.

4.4 Limitations of the Method

Although doctrinal research is valuable in terms of understanding legal frameworks and the intent behind a particular regulatory intervention, it does not take a practical step in evaluating the economic or social consequences of a particular regulatory action. In such a way, conclusions of this paper can be interpreted as analytically based and not tested empirically. Nevertheless, the analysis of doctrine is critical to comprehending the ways that law presents the prospects and constraints of innovation in practise.

5. Findings and Discussion

This section outlines and comments on the major findings obtained as a result of the doctrinal analysis of legal frameworks that regulate the FinTech and AI-enabled business services. The discussion illustrates the active design of innovation-driven business transformation by law, using a combination of various, and interrelated mechanisms instead of acting as external constraint only.

5.1 Law as Innovation Infrastructure Rather Than Mere Constraint

One of the key conclusions of the research is that law is an innovation infrastructure of FinTech and AI-based services. As opposed to the perception that regulation only slows the innovation process, legal structures give innovative business models the predictability and stability they need in order to develop and grow. This is because in the financial services, licencing regimes, prudential requirements, and consumer protection rules create the baseline of trust that digital financial products are unlikely to pick up in the market (Arner et al., 2017).

In the FinTech industry, such as digital payment services and online lending, the legalisation of electronic payments, enforceability of digital contracts, and the definition of who is liable are crucial factors that these companies cannot do without. These legal bases cut down on the cost of doing business and enable the firms to concentrate on the perfecting of technology and customer experience as opposed to being caught in a legal maze. Likewise, AI-based service providers rely on explicit data governance regulations to justify data collection, processing, and sharing activities, which support machine learning systems (Cohen, 2019).

The analysis indicates that well-articulated legal systems in coherent settings are facilitating innovation by reducing uncertainty and providing regulatory expectations. On the other hand, ineffective regulation or/and ambiguous regulation can result in compliance risk, a lack of incentive towards long-term investment, and an advantage to those who have more legal resources to navigate the system.

5.2 Risk-Based and Adaptive Regulation as Catalysts for Business Transformation

The second significant result is the increased significance of risk-based and adaptive regulation mechanisms in the formation of the outcomes of innovation. The old-fashioned prescriptive regulation with the focus on the set of rules specific to certain technologies has not been suitable to be applied to fast-changing industries like FinTech and AI-enabled services. To counteract it, the regulators have turned to risk-based supervision, principles-based regulation, and adaptive tools aimed at embracing uncertainty (Baldwin et al., 2011).

Regulatory sandboxes represent this change in FinTech. The sandboxes decrease regulatory uncertainty and provide regulators with real-time information about new technologies by ensuring that firms are able to test innovative products in controlled environments. Theoretical work on sandbox models shows that they should be seen as valuable not only because of transitory regulatory facilitation but due to the designed conversation that they establish between innovators and regulators (Zetzsche et al., 2017b). The impact of this interaction is on product design, compliance architecture, and long-term business strategy.

Non-binding guidelines, ethical frameworks, and sector-specific standards serve the same purpose in the adaptive governance mechanisms in AI-enabled services. These tools do not entrap innovation in strict legalist terms, but offer guidance on a directional basis. Nonetheless, the discussion also indicates a contradiction, to the extent that flexibility will help enhance innovation, but over-use of soft law may undermine the accountability and legal certainty, especially in cases where AI systems impact society or the economy in a significant manner (Brownsword, 2019).

It does not indicate that flexibility ought to be in place of formal regulation since what is observed here is that adaptive tools need to be incorporated into a larger legal framework that is enforceable, transparent, and publicly monitored.

5.3 Data Protection/Cybersecurity as Enablers of Organisational Change.

A third observation is the revolution of the law of data protection and cybersecurity on the business organisation and strategy. With the FinTech and AI-based services, data is the key material behind the innovation. The legal requirements regarding the processing of personal data, security, and the notification of breaches have thus become the main focus of the system design and distribution of duties at firms.

The data protection regime based on the doctrinal analysis reveals that the contemporary data protection rules are increasingly forcing firms to implement the proactive governance framework that includes data protection impact assessments, privacy-by-design, and internal accountability systems. The requirements are not just outside compliance costs; they redefine the internal decision-making formations and technology frameworks (Wachter et al., 2017).

The obligations of transparency, fairness and explainability in AI-driven services have a direct implication on algorithm design and deployment. The companies are forced to invest in documentation, auditability, and human oversight systems, which subsequently affect the rate and the trend of innovation. Although the requirements might seem limiting at first, the analysis indicates that they can also minimise the long-term legal and reputational risk, which contributes to the transition to sustainable business (Floridi et al., 2018).

5.4 The Competition Law and the Contestability of the Innovation Markets

The other important finding is connected to the contribution of competition law to the occurrence of innovation-driven transformation of businesses. Much of the FinTech and AI enabled service markets are being described in terms of network effect, data concentration, and platform dominance. Competition law is very vital in these environments in that it is able to define how innovation can be a contestable and accessible or it becomes a monopoly of a limited number of dominant companies.

As shown in the doctrinal analysis, the competition policy thinking has changed, and now more attention is paid to access to data, interoperability, and exclusionary behaviour in digital markets. Competition agencies are becoming more aware that market influence in innovation-driven industries may not be seen in terms of price impacts, yet by manipulation of data, algorithms, and digital infrastructure (Khan, 2017).

This changing competition environment affects business strategic decisions about mergers, partnerships and platform governance. Companies that have been involved in business transformation should consequently consider competition law in their innovation-based plans, especially in the process of scaling AI-based platforms or financial ecosystem.

5.5 Strategy Capabilities Reflected in Corporate Governance and Compliance.

Another implication of this study is the growing importance of corporate governance and compliance as strategic capabilities in sectors where innovation assumes a central role. In FinTech and AI-enabled services, regulatory requirements go beyond the technical compliance with regulation to include organisational responsibility, board governance, and risk management.

The doctrinal analysis indicates that there is a growing pressure of the senior management and the boards to take up the risk of technology, data governance, and the ethical aspect. This change has been influenced by the wider understanding that any risks that are related to innovation cannot be successfully handled at the operational level. In their place, they need to be strategically monitored and incorporated into corporate governance structures (Cath, 2018).

Those firms which consider compliance as dynamic capability as part of the organisational culture and decision-making are more apt to adjust to the regulatory change and public scrutiny. This discovery highlights the larger argument of the article that law does not only influences the external market conditions but the internal organisational change.

5.6 Synthesis of Findings

The combination of these findings demonstrates that law possesses a wide array of overlapping mechanisms that influence innovation-driven business change. In FinTech and AI-sensitive services, the incentives are predetermined by the law, predatory behaviour is restricted, ambiguity is managed, and the markets are organised. These structures should be compatible with the realities of business, flexible and consistent across various domains to work.

This discussion validates the perception that innovation and regulation are not in a contrastive relationship to each other but rather one that is mutually dependent within a functional innovation ecosystem.  Where law is designed and executed wisely, it can aid in guiding business change towards financially viable, morally acceptable, and legally valid results.

6. Recommendations

This article analysis highlights that innovation is best regulated by purposeful coordination of the legal design and business transformation strategies.  The following recommendations are offered for both regulators and businesses operating in FinTech and AI-enabled service sectors.

6.1 Recommendations for Regulators

To start with, regulators must proceed towards risk-based and proportionate regulation systems that acknowledge the heterogeneity of innovation. Not every FinTech product or AI application is equally risky and the intensity of regulation should reflect this difference. The comprehensive presence of clear risk-tiering systems can prevent excessive regulation of low-risk innovation and provide a strong level of control over high-impact technologies (Baldwin et al., 2011).

Second, the concept of regulatory sandboxes and innovation hubs is supposed to be institutionalised as a method of learning instead of being regarded as extraordinary or temporary projects. The regulators are to make sure that the experience acquired during sandbox participation is turned into permanent regulatory change, such as new guidance, clearer licensing routes, and new supervisory requirements. Lacking these feedback loops, sandboxes will be symbolic instead of transformative (Zetzsche et al., 2017b).

Third, regulators must focus on inter-agency coordination especially when the innovation cuts across legal jurisdictions. Regulatory fragmentation in FinTech and AI-enabled services may produce unequal obligations and compliance ambiguity due to the incompatibility of financial regulation, data protection, and competition regulation authorities. Synchronized direction and combined supervisory efforts would enhance consistency and lessen the unjustifiable regulatory friction.

Fourth, more attention is to be paid to outcome-oriented and principles-oriented regulation in the field of high technological dynamism. Although rules are still needed to be enforced, the top-level principles associated with fairness, transparency, security, and accountability offer flexibility without compromising the normative clarity. Nevertheless, interpretive guidance and enforcement practice should be adopted to support principles-based approaches to promote predictability.

Lastly, regulators ought to spend on institutional capacity-building in terms of technical expertise and data analytics. The proper regulation of AI systems and intricate digital financial products requires not only law, but also the capacity of the regulators to make sense of and query their technological design decisions.

6.2 Recommendations for Businesses

For businesses, the key recommendation that can be offered is to rethink legal compliance as a strategic resource as opposed to a reactive expense. Legal analysis must be part of the product design and system architecture of a company that is in the process of an innovative change, particularly in the framework of data management, consumer protection, and algorithm responsibility.

Second, firms ought to enhance corporate governance systems to indicate the strategic importance of innovation-related risk. The technology, data, and regulatory compliance board-level oversight is becoming more common in FinTech and AI-intensive companies. Good accountability frameworks promote decision making and project organisational seriousness to the regulators and stakeholders.

Third, businesses need to adopt compliance-by-design policies, integrating legal requirements into technology systems and business processes. This includes explainability, bias minimization, and auditability mechanisms of AI-enabled services. In FinTech, it is customer due diligence, transaction surveillance and cybersecurity. These measures, in addition to reducing the risk of regulation, contribute to greater trust and credibility in the market.

Fourth, business should engage in active interaction with regulators consultations, innovation hubs, and industry forums. Preemptive communication allows businesses to shape regulatory expertise and gain advance information about emerging compliance expectations. This kind of discussion is especially important in fields where the law is just developing.

Lastly, companies must also invest in in-house legal and regulatory strength, including interdisciplinary skill that spans over law, technology, and business strategy. As sectoral boundaries continue to dissolve due to innovation, those organisations that can make sense of and operationalise complex regulatory demands will find it easier to adapt and compete better.

7. Conclusion

In this article, the role of law in influencing innovation-driven business change was discussed, with special attention paid to the fields of FinTech and AI-assisted service-based businesses.

This article has reviewed the contribution of law for the development of innovation-led business transformation focussing on Fin tech and AI enabled service industries. It has shown by analysing doctrine that law is not just an after-the-fact reactive constraint on the engines of innovation, but a force constitutive of markets, managing risk, facilitating trust.

As the findings show, legal frameworks impact on business transformation in several interrelated ways: they provide infrastructure for business change; they regulate uncertainty by establishing adaptive regulation; they define data governance and competition; they redefine expectations in corporate governance. Where regulation is coherent, risk sensitive and adaptive, this can provide support for innovation, which is both economically viable as well as socially responsible. On the other hand, fragmented or inflexible legal frameworks may work against transformation and/or lock in market power.

The article argues that sustainable innovation depends on the alignment between regulatory design and business strategy. For regulators, this requires embracing learning-oriented, coordinated, and proportionate approaches to governance. For businesses, it demands the integration of legal compliance into the core of transformation initiatives, rather than treating it as an afterthought.

Finally, innovation regulation is not a question of control or creativity. It is concerned with creating legal structures that facilitate change in a manner that maintains trust, safeguards public interests, and facilitates the creation of value in the long term. With the continued transformation of the world economy by FinTech and AI-enabled services, the role of law in defining their direction shall be both essential and disputed.

Author Contributions: All authors contributed to this research.

Funding: Not applicable.

Conflict of Interest: The authors declare no conflict of interest.

Informed Consent Statement/Ethics Approval: Not applicable.

Declaration of Generative AI and AI-assisted Technologies: This study has not used any generative AI tools or technologies in the preparation of this manuscript.