E-ISSN:2583-1747

Research Article

Viksit Bharat

Management Journal for Advanced Research

2026 Volume 6 Number 3 June
Publisherwww.singhpublication.com

AI Governance Frameworks and Financial Market Regulation: A Comparative Study of Germany, Qatar, and Implications for India's Viksit Bharat @2047

Jabeen N1*
DOI:10.54741/MJAR/6.3.2026.313

1* Niloufer Jabeen, Doctoral Researcher, Department of Political Science and International Relations, Adamas University, Kolkata, West Bengal, India.

This paper makes a comparative analysis of the governance of artificial intelligence (AI) systems in Germany and Qatar, and examines the possibilities these two structurally different models can offer to India as it is developing its own model for the governance of AI-powered financial markets with the development vision of Viksit Bharat at 2047. In Germany, AI falls under the European Union's wide-ranging AI Act (Regulation (EU) 2024/1689), which categorizes AI systems based on risk and mandates specific rules on transparency, human oversight and data governance in critical industries such as credit scoring and algorithmic trading. Qatar takes a very different approach, using AI governance as an instrument of economic diversification and diplomatic positioning, most recently through the establishment of Qai under Qatar Investment Authority, and the country's joining of the Pax Silica coalition in early 2026. The analysis builds upon the concept of "soft power" as extended into the digital world by the notion of "algorithm power" and posits that government of AI is becoming more and more a geopolitical than a supervisory act. The new regulatory framework in India, which includes the Reserve Bank of India's FREE-AI framework, the Securities and Exchange Board of India's guidelines for securities markets and the India AI Governance Guidelines provided by the Ministry of Electronics and Information Technology, is explored in this comparative framework. Finally, the paper suggests that India has the democratic mandate, institutional scale, and development rationale to shift from being a follower of regulation to a global norm-setter for the Global South, if it overcomes the existing fragmentation of its regulatory framework and the safeguards for financial inclusion are translated into enforceable first-order obligations as opposed to principles.

Keywords: AI governance, financial regulation, comparative policy, germany, qatar, india, viksit bharat, fintech, algorithmic soft power, FREE-AI, EU AI act

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Niloufer Jabeen, Doctoral Researcher, Department of Political Science and International Relations, Adamas University, Kolkata, West Bengal, India.
Email: 		Jabeen N, AI Governance Frameworks and Financial Market Regulation: A Comparative Study of Germany, Qatar, and Implications for India's Viksit Bharat @2047. Manag J Adv Res. 2026;6(3):40-48.
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© 2026 by Jabeen N and Published by Singh Publication. This is an Open Access article licensed under a Creative Commons Attribution 4.0 International License https://creativecommons.org/licenses/by/4.0/ unported [CC BY 4.0].

Manag J Adv Res 2026;6(3)40
Download PDFBack To Article1. Introduction2. Literature
Review3. Objectives4. Methodology5. Analysis and
Discussion6. Findings of
the Study7. Practical
Implications and
Future Research
Scope8. ConclusionReferences
Jabeen N. AI Governance Frameworks and Financial Market Regulation

1. Introduction

AI has evolved from the fringes to the core of financial services in the last 20 years. In fact, today, algorithmic trading engines now dominate a significant fraction of the daily equity market volume in most major economies, while AI-driven credit scoring systems determine access to credit for hundreds of millions of people, and robo-advisory platforms are making inroads on every conventional equity portfolio management platform. These progress have been well documented in the academic literature (Cao, 2020; IMF, 2024; OECD, 2021) and are not just technical in nature. They reshape institutional arrangements that enable states to govern their economies, create new kinds of systemic risk and provide new venues for financial exclusion and discrimination to be programmed.

In addition to these tangible stakes, AI governance in finance has now clearly taken a geopolitical turn. Values that states incorporate into their policies, institutional frameworks they build, and international coalitions they form to monitor the use of AI in financial policies and systems are now indicators of economic competitiveness and soft power. Values embedded in policies, institutional structures created, and international coalitions joined by states to manage AI in financial policies and systems are now indicators of economic competitiveness and soft power. Credible and coherent governance structures can lead to attracting investment, shaping international standard-setting organisations and disseminating governance philosophies to less powerful trade partners and states. Lacking these can result in regulatory arbitrage and capital movement toward less-governed states, and loss of meaningful voice in new global deliberations.

It's a momentous time in India's context. It has one of the most innovative financial technology sectors globally, and its digital public infrastructure, based on the Unified Payments Interface, Aadhaar identity system and Account Aggregator framework, offers a particularly robust institutional base for AI-based financial services (Chakravorti, 2024). However, its regulation of AI in finance is fragmented among several institutions and is still being streamlined. This consolidation needs to proceed with urgency and deliberateness into a self-reliant, technologically

advanced and inclusively developed India, as spelt out in the Viksit Bharat @2047 vision of the government (NITI Aayog, 2025; Government of India, 2020).

It's a momentous time in India's context. It has one of the most innovative financial technology industry in the world and its digital public infrastructure (DPI) – based on the Unified Payments interface, Aadhaar identity system and Account Aggregator framework (AA) – has a particularly strong institutional foundation for AI for financial services (Chakravorti, 2024). Its governance of AI in the financial sector is fragmented between multiple institutions, and is still undergoing streamlining. This consolidation needs to proceed with urgency and deliberateness into a self-reliant, technologically advanced and inclusively developed India, as spelt out in the Viksit Bharat @2047 vision of the government (NITI Aayog, 2025; Government of India, 2020).

The rest of the paper is structured as follows: A scholarly literature review is the focus of Section 2. The research objectives are revealed in Section 3. The methodological approach is explained in Section 4. The comparative analysis is given in section 5. The substantive findings are reported in Section 6. A section on practical implications and suggestions for further research are discussed in Section 7. The concluding reflections are presented in Section 8.

2. Literature Review

In the last decade, the scholarly literature on AI and financial governance has expanded significantly, based on political science, economics, law and science and technology studies. Cao (2020) presented one of the most systematic early reports on the impact of machine learning on the investment philosophy, risk analysis and financial intermediation of financial institutions, and found that AI is not just taking over manual tasks but also transforming the decision-making processes of financial institutions. The Global Financial Stability Report (2024) extended this analysis by the International Monetary Fund (IMF), which stated that AI's impact on efficiency and credit access would eventually lead to other issues, such as correlated model failures, algorithmic herding and opacity in credit decisions, that traditional supervisory instruments are not well suited to address.


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Liu and Wang (2024) provide a basic comparative regulatory analysis of opposing AI governance traditions, which can be characterized as either rights-oriented or democratic-governance-oriented, and as either industrial-development-oriented. In their work on Germany and China they provide light to the institutional and ideological influences on regulatory design. Therefore, the European Union's regulatory standards also can spread beyond its borders – as suggested by Bradford (2020), the so-called ‘Brussels effect', which is when firms seeking access to the EU single market must meet EU elements of the rules even when the rules differ in their home markets. Similar analytical frameworks are used to examine the Chinese context by Roberts and colleagues (2021), who trace the manner in which state-centric forms of governance in Al can both stimulate deployment and limit accountability.

In addition to the hard power literature, the soft power literature offers a complimentary perspective on why states go beyond domestic regulatory uses in their investment in AI governance. For Nye (2004, 2025), the soft power is the capacity to influence the preference of others by induction rather than force, namely in the areas of cultural attraction, institutional models, and norms. Waldemar Bjola (2022) and Manor (2019) take the concept of soft power into the digital arena introducing the concept of algorithmic soft power, meaning how States assert influence in international arenas, how States draw foreign investment and how they become credible partners for cooperation. The governance strategy that Qatar proceeded with (and that led to the creation of Qai under the Qatar Investment Authority as well as the strategy of making sure that the Qai is matched to the Pax Silica coalition as described in this article by Albous et al. (2025) and Helberg (2026)) is an empirically tractable example of this phenomenon.

Over the past few years the regulatory regime in India has grown far more concrete. The RBI, 2025, outlines seven principles and twenty-six recommendations across six governance pillars that cover the entire spectrum of AI's use in the financial industry in its FREE-AI framework. The regulation of the capital markets is the latest to be focused upon by the Securities and Exchange Board of India (SEBI, 2025). The regulations set forth by the Ministry of Electronics and Information Technology (MeitY, 2025) offer a cross-sector approach, and the report on AI for Viksit Bharat by NITI Aayog places

AI governance within India's developmental programme. As Chakravorti (2024) shows, the DPIDF gives rise to unique structural assets for AI governance in India, notably in data and identity systems which underpin financial AI systems. The framework for co-production presented by Jasanoff (2016) establishes a conceptual framework for examining who the values are embedded in AI governance frameworks and on whose interests they act.

The existing literature on the Global South and AI governance is fairly sparse and is expanding. From a different perspective, that of local development, Pérez Martínez (2024) warns against blindly copying governance models that have been developed for and in advanced industrial countries into developing country settings because of the different distributional implications when AI fails. This is a more constructive argument developed by Arun (2022) who explores the possibilities for Global South states to engage in the process of norm construction, instead of simply receiving pre-packaged norms from elsewhere. The contributions are directly pertinent to India's ambition to be a leader in the governance of AI in the Global South, which has recently been enabled by the Government of India's hosting of the AI Impact Summit of the Global South in February 2026 (Government of India, 2026). In addition, Korinek's (2023) methodological article regarding the use of AI tools in economic research offers a wider context for the type of evidence collection that is needed for strong regulation.

3. Objectives

This paper consists of four main research goals.

The first is to describe the principles, designs and measures of financial market regulation in Germany and Qatar, focusing on the rationale, institutional structure, and instruments used in the two models which differentiate them.

The second is to utilize the soft power framework by Joseph Nye to examine the geopolitical aspects of AI governance decisions and to analyse the global impact of the governance model on the host countries.

The third goal was to compare the case of emerging regulatory structure for AI in financial markets in India with the above sketched cases of Germany


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and Qatar, bringing out both strengths and structural deficiencies in the existing regulatory framework.

The fourth goal is to draw strategic/policy insights for India in building a unique model of Governance for AI responsive to India's developmental goals and the vision for norm leadership in the Global South under the banner of Viksit Bharat by 2047.

4. Methodology

The qualitative comparative policy analysis method is applied mainly in this study with the reference of Collier (1993) and Ragin (2014). It is appropriate to the research purposes of the study since it allows for a comprehensive analysis of governance structures in various jurisdictional and institutional settings while being sensitive to the influence of political economy, legal history and development priorities on governance decisions. In contrast to a focus on statistical generalisability, the hallmark of qualitative comparative analysis is the search for the "governing logics" and "institutional conditions" that can account for the different outcomes of governance — a goal that is directly relevant to this study, which focuses on the emergence of structurally distinct governance arrangements to the same regulatory challenge in Germany, Qatar, and India.

The analysis is based on three types of primary sources. The European Commission Guidelines (European Commission, 2025) and Regulation (EU) 2024/1689 are EU binding regulatory instruments. RBI's FREE-AI report (RBI, 2025), SEBI's consultation paper (SEBI, 2025), MeitY's IndiaAI Governance Guidelines (MeitY, 2025), NITI Aayog's AI for Viksit Bharat report (NITI Aayog, 2025) are some of the official policy documents. Intergovernmental and multilateral publications comprise the IMF Reports (2024, 2025), OECD Reports (2021). The interpretive framework for the analysis of the primary sources is provided by secondary sources taken from peer-reviewed journals and policy research publications.

The choice of Germany and Qatar as comparative countries is theoretically based. Germany is the representative of the European regulatory tradition: a rule of law-based approach with democratic accountability, risk-based regulation, integrated in supranational institutional structures.

That said, Qatar embodies the strategy of statecraft, which is a small, resource-rich nation that has seen AI governance largely as an economic diversification and geopolitical influence tool. These cases, however, are not similar cases that seek to control for contextual variations; rather, they are analytically contrasting cases that are chosen to highlight the array of possible governance strategies states can use and provide a maximally informative background to compare India's developing strategy.

5. Analysis and Discussion

5.1: The German Model: Risk Classification, Rights Protection, and Regulatory Harmonisation

Germany's strategy for AI governance in financial markets cannot be divorced from the regulatory framework of the European Union. The AI Act (Regulation (EU) 2024/1689), adopted by the European Parliament and Council in June 2024, introduces a four-tiered risk classification system, with AI applications categorised as unacceptable, high, limited or minimal risk, and the obligations for compliance proportionate to each risk level (European Parliament and Council, 2024). The financial sector is part of the high-risk sector and the mandatory compliance regulation of systems in that sector will take effect from August 2026 (European Commission, 2025).

High-risk AI systems have a long list of substantive requirements. This requires operators to carry out conformity assessments before deployment, keep comprehensive technical documentation, establish continuous risk management systems, inform the users about the use of AI, ensure that there is a human override mechanism, and adhere to data governance requirements that minimize potential bias and error (Liu & Wang, 2024). They are based on the regulatory approach of regarding AI as a potent but volatile tool whose use in high-stakes areas should be regulated within institutional safeguards commensurate with its potential impact.

It is important to understand that the impact of the AI Act does not stop at the border of Europe. As Bradford (2020) shows, there is a concept of the Brussels effect, according to which European regulatory standards are followed or accepted in the countries outside of the EU for companies wishing to gain access to the European single market.


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It implies direct consequences in the context of the fintech industry of India, where the companies want to enter the European financial market, and have to comply with the standards of AI governance developed in Europe despite any existing domestic regulations in India.

The German Act on Artificial Intelligence also introduces a more comprehensive understanding of AI as a field that demands more than just careful oversight; it needs to be held accountable to democratic principles and safeguarded against infringement on fundamental rights. This perspective – a product of Germany's constitutional history and the EU's founding principle of the rule of law – sees AI governance as a reflection of a social contract between citizens and states rather than a tool to manage financial risk. This is the normativeness that is the hallmark of the German model and makes it relevant as a benchmark for Indian democratic regulatory aspirations.

5.2: The Qatari Model: Algorithmic Soft Power and Economic Statecraft

There is a different strategic approach to AI governance in Qatar. Historically, a small state whose foreign relations have historically been built on hydrocarbon wealth and strategic partnerships, Qatar has invested significantly in AI as a technological capacity as well as a governance narrative, using its AI promotion to diversify its economy and build its influence in international, geopolitical arenas (Albous et al., 2025; IMF, 2025). The establishment of Qai, the national AI firm under Qatar's sovereign wealth fund, Qatari Investment Authority, in December 2025 is a prime example of this approach: by establishing a national AI firm under a sovereign wealth institution, Qatar combines technological innovation with national identity and geopolitical positioning in one stroke of the pen.

In January 2026, Qatar joined the Pax Silica coalition, a U.S.-led alliance of states that is seen by U.S. officials as a political playbook in the modern international system, and is more commonly known as silicon statecraft (Helberg, 2026). Qatar establishes its own reliability to global capital markets and gains access to cutting-edge AI technologies via exclusive partnership deals by working with states that are top producers of AI.

Qatar demonstrates reliability to global capital markets and also gains access to the latest AI technologies through high-level partnership schemes by working together with leading AI-producing states and agreeing to interoperable governance standards.

Drawing on Nye's (2004, 2025) work on soft power, the concept of algorithmic soft power, developed by Bjola (2022) and Manor (2019), encapsulates how Qatar asserts influence in this way. Instead of asserting itself with military force or direct economic pressure, Qatar influences international financial norms by making its institutions for AI governance models that should be emulated, and its infrastructure for sovereign wealth a preferred partner for capital globally. It is a unique mode of power in the digital age: the ability to shape rules and standards for AI-influenced financial markets through a respectful and clear presence within the global governance framework without the territorial, demographic and military force associated with traditional power projection.

It is important to note that the Qatari model introduces governance issues that can be ignored by the focus on projecting a brand image. There is a risk for smaller states to use AI governance to position themselves for attracting foreign investment and ensuring diplomatic alignment while neglecting domestic accountability and rights protection. Smaller states that use AI governance to pursue positioning for foreign investment may end up sacrificing considerations for domestic accountability and rights protection for foreign positioning. The case of Qatar is instructive for India in this regard because it shows the opportunities and the constraints of the soft power route, the very fact of its capacity for international influence is real, but it needs to have credible domestic governance institutions to support it in the long term.

5.3 India's Regulatory Architecture: Navigating Between Models

This section examines India's regulatory architecture in the context of the two models. This section draws out India's regulatory architecture between the two models.

The AI governance framework in the financial sector in India is not just a European rights-based or a Qatari brand-based model. It is an attempt to develop a unique national vision on regulations which aligns with India's developmental agenda,


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which aligns with India's developmental agenda, democratic framework and strategic autonomy. To grasp the framework, it is important to take into account both the institutional setting in which it is being built and its substantive aspects.

The most developed regulatory document so far is the Framework for Responsible and Ethical Enablement of Artificial Intelligence in the financial sector (FREE-AI framework, 2025) that was released by the Reserve Bank of India (RBI). FREE-AI is organised under seven principles (also known as Sutras) and twenty-six recommendations across six pillars of governance, covering the entire lifecycle of using AI in financial institutions: Data governance, Model validation, Explainability, Human oversight, Grievance redressal, and Regulatory reporting. Also significant is the framework's focus on algorithmic accountability and safeguarding financially vulnerable users, which indicates a recognition that the financial inclusion agenda in India, with hundreds of millions of people only recently and precariously included, cannot be compromised for efficiency gains from unchecked use of AI.

The recently released SEBI consultation paper titled, “Guidelines for the use of responsible artificial intelligence and machine learning in Indian securities markets” brings the regulatory oversight of the capital markets towards algorithmic trading systems, AI based surveillance tools and investor facing applications. MeitY's IndiaAI Governance Guidelines issued in November 2025 offer a cross-sectoral scaffolding that places the governance of financial AI into a wider policy framework of the nation. These governance questions are part of India's development aspirations as elaborated in NITI Aayog's report titled 'AI for Viksit Bharat' (2025), which highlights the potential of AI to contribute to the nation's economic development while also underscoring the risks of its governance failures leading to an increase in inequality. Most states in the Global South do not have institutional building blocks for AI governance such as the Unified Payments Interface, the Aadhaar and the Account Aggregator framework, which are significant state investments in shared financial data architecture (Chakravorti, 2024).

The Government of India's vision of India leading the way for governance of AI in the Global South was institutionalized in the February 2026,

hosting the world's first AI Impact Summit of the Global South in New Delhi, on the basis of the Sanskrit principle Sarvajana Hitaya, Sarvajana Sukhaya — welfare for all, happiness for all (Government of India, 2026). The recognition that this initiative entails echoes Pérez Martínez's 2024 and Arun's 2022 arguments that it is not enough for Global South states to simply be governed by rules created by and for advanced industrial economies, but necessitates their own active engagement in shaping norms that are relevant to their own developmental contexts, cultural values, and democratic commitments. Self-reliance is India's political vision for this normative aspiration, expressed through the Atmanirbhar Bharat framework (Government of India, 2020): a vision of strategic autonomy in defence, manufacturing and digital infrastructure that must be extended to norms in AI-governed economic development.

One of the biggest issues in the AI governance landscape is the fragmentation of the Indian market. The financial institutions operating in multiple regulated domains are exposed to the risks of jurisdiction ambiguity, compliance duplication and regulatory gaps due to the presence of multiple policies and regulations developed by different institutions, based on different legislative mandates, and with no coordination among the relevant authorities. The governance structure is well-developed in some aspects, but it is lacking in coherence.

6. Findings of the Study

The comparative analysis brings forth four substantive conclusions with potentially implications for the existing literature on AI governance as well as for Indian policymakers.

Firstly, AI governance structures in financial markets are not "neutral" regulatory tools. They are imbued with specific views of the role of the state, the market, and the citizen and reflect the institutional experiences, political cultures and developmental agendas of the jurisdictions that generate them. The AI Act in Germany introduces a notion of AI as a field in need of democratic control and respect for fundamental rights. AI is integrated as a means for national economic transformation and international brand enhancement in Qatar's governance model. The new framework in India is starting to incorporate a vision of AI as a tool of inclusive development;


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however, the institutions for carrying this vision into action and practice are still under construction.

Second, it offers real analytical leverage for understanding state action in the field of AI governance: the soft power of algorithms. Second, the notion of algorithmic soft power gives actual analytical leverage for understanding state action in the area of AI governance: algorithmic soft power. Germany has influence over global financial norms, albeit in different ways: by binding regulation and its extraterritorial effects; Qatar has influence by its strategic investments and diplomatic positioning, or demonstration effects. This is possible for India with its institutional size, democratic legitimacy, and rationale for development in the Global South, but it demands not just the formulation of the principles of governance (however elegant) but the creation of institutions for implementation with credible enforcement powers.

Third, the financial inclusion aspect of AI governance is a specific challenge in India and sets the country apart significantly from Germany and Qatar. The regulatory structure in Germany is more geared towards safeguarding consumers and companies in a well-developed financial market; whereas Qatar's regulation is more focused on drawing in sophisticated foreign capital. India, on the other hand, operates a financial system with hundreds of millions of people who are new to formal finance, and where the risk of discrimination, predatory pricing, and opaque credit denial are likewise high. Protecting these populations as first order concerns, not afterthoughts, should be a fundamental component of an AI governance framework suitable to India's context, which should go beyond the consumer protection frameworks that apply to more mature markets.

Fourth, the Indian regulatory framework, although conceptually very advanced, is subject to multiple regulators and ministries that create coordination problems. Without a seamless inter-regulatory coordination process between the RBI, SEBI, the Insurance Regulatory and Development Authority of India, the Pension Fund Regulatory and Development Authority, MeitY and NITI Aayog, the potential for regulatory gaps and compliance requirements could hinder the very financial innovation that India is trying to responsibly regulate.

7. Practical Implications and Future Research Scope

The results have a number of practical implications for policy makers and financial regulators. The immediate institutional reform needed as part of this is creating a single body for AI governance coordination, like the Financial Stability and Development Council in prudential space to ensure coordination of the activities of the RBI, SEBI, IRDAI, and PFRDA with the cross-sectoral governance framework under development by the MeitY and NITI Aayog, and to bridge the existing jurisdictional overlaps and gaps, and to minimise overlapping compliance requirements for regulated financial institutions.

The principles of financial inclusion should be made explicit, and written as binding regulations, not aspirational goals, for regulatory design. This includes algorithmic fairness checks for AI in credit decisions, insurance underwriting, and investment advice, focusing on caste, gender, geography and income risk factors. The need to safeguard vulnerable populations in the FREE-AI framework should be translated into enforceable standards with easy access to grievance processes.

To advance India's ambition of being a leader for Global South norm-setting on AI governance, a systematic programme of diplomatic outreach to share the FREE-AI framework, IndiaAI Governance Guidelines and the AI Impact Summit architecture with counterpart regulators and governments in other Global South jurisdictions should be implemented. It is in India's interest and others in the Global South to showcase the Indian model of government, based on democratic values, financial inclusion needs and developmental aspirations as an alternative to both the compliance driven European framework and the European brand projection model from Qatar.

The analysis could be continued in a number of interesting ways in the future. The evidence on the difference between the regulatory aspiration and institutional reality would be gained once adequate time has elapsed for institutional responses to be evident for the implementation of FREE-AI and the AI guidelines of SEBI. Comparisons with other jurisdictions in the Global South, including the approach to AI governance in finance in Brazil, South Africa and Indonesia, could provide additional inputs and challenges to this analytical framework.


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First-hand accounts from financial regulators, technology companies, civil society organisations and those most impacted by AI-driven financial decisions would enable primary, qualitative research, which would capture the ground-level perspectives that policy documents don't.

8. Conclusion

This paper has proposed that AI governance in financial markets can be seen as regulatory tools, geopolitical instruments, and declarations of national developmental ideas simultaneously. The German experience shows how a tradition of the rule-of-law incorporated in a supranational regulatory framework can produce binding standards of global normative force via the Brussels effect. The Qatari case is a testament to the potential of AI governance as a means of economic diversification and international influence for smaller states via the algorithmic soft power. With its scale, democratic legitimacy, and emerging technological prowess, India has a unique opportunity to become a norm-setter in the governance of AI-fuelled financial markets, rather than merely a policy borrower, if it can build up the institutional framework for this goal.

For this ambition, a developed, self-reliant and inclusively organised India with financial systems run by AI frameworks for the benefit of the many, not the few, and internationally recognised regulatory institutions as models of responsible innovation, is a meaningful temporal horizon that can be realised in the vision of Viksit Bharat @2047. To achieve that horizon, the implementation of the principles India has already articulated – the seven Sutras, twenty six recommendations, and the more generic governance commitments that exist on paper in India and have to be brought to life through the building of regulatory institutions with capacity, independence and accountability, and through the consistent political commitment. India's AI governance in finance is just beginning: the most impactful decisions are yet to come.

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