India Proposes AI Framework for Financial Sector Governance

[2025] 178 taxmann.com 612 (Article)

1. Introduction

On August 13, 2025, the Reserve Bank of India (“RBI”) released the ‘Free-AI Committee Report’ (“FREE AI Report”), which deals with the use and development of artificial intelligence (“AI”) models by entities regulated by the RBI (such entities, “REs”).

Earlier, in December 2024, the RBI had constituted a committee to (i) analyze the use of AI in the Indian financial sector, and (ii) recommend a sector-specific framework for the responsible and ethical enablement of AI models (such framework, “FREE AI”). Pursuant to the release of the FREE AI Report, preliminary reports suggest that the proposed framework may require REs to undertake significant investments and operational changes, including with respect to new governance structures and capacity-building measures.

Banks – a key RE category – have been integrating AI models in their business operations for some time. However, such models are largely unregulated due to the absence of a dedicated AI-regulation in the country. In that regard, the FREE AI Report discusses the basis for regulating AI use in the Indian financial sector in the future, pursuant to principles of transparency, responsibility, and ethics.

2. Background

With respect to regulating AI, India’s stance has fluctuated between non-intervention (to facilitate growth) and caution (to protect AI users from harm). At present, it appears that India has decided to maintain a pro-innovation approach to promote the development and deployment of safe, responsible, and trustworthy AI models for the purpose of enhancing the quality of life for all. For a broad overview of Indian regulatory developments on AI, see our note here. For a discussion on the challenges associated with AI regulation, including for the purpose of balancing promotion with protection, see our notes here and here.

In March 2024, the Government of India launched the India AI mission with an outlay of INR 103.72 billion. This was followed by an advisory issued by the Ministry of Electronics and Information Technology (“MeitY”), which required intermediaries to comply with due diligence obligations to ensure user safety (for a detailed discussion on the MeitY advisory, see our note here). Separately, in October 2023, an expert group constituted by the MeitY released the first edition of its India AI Report, which laid the groundwork for developing an AI ecosystem in India while ensuring proper regulation based on high standards of governance, protection of intellectual property rights, and deployment of responsible AI systems (for an overview of key AI-related risks and considerations, see our note here).

3. AI in the Financial Sector

Financial sector firms, both globally and in India, have adopted the use of AI more quickly than other industries. Such adoption has extended to back-end and front-facing operations, including those related to sales, customer and employee experiences, fraud and risk management, and technology development.
There are several benefits which are expected to arise from AI integration in the Indian financial sector. Such integration is likely to enhance credit access, especially for people situated in remote areas. In addition, AI-powered chatbots can reduce the cost of operations for financial sector entities and ensure seamless and reliable communications. Further, AI can be employed by financial institutions for cybersecurity – especially relevant in light of India’s robust digital payments ecosystem. Lastly, AI can assist REs to fulfil their regulatory obligations, including those related to Know-Your-Customer (KYC).

However, an excessive reliance on AI without adequate supervision could pose threats, including those stemming from the potentially exclusionary nature of AI models on account of skewed training data, leading to issues of accessibility and discrimination. The FREE AI Report notes that AI models are ‘western-centric’ and largely based on the English language. However, the customers of REs hail from diverse regions of the country and may not be proficient in English. Accordingly, bespoke AI models need to be developed for customers who speak other languages. While there exist strong reasons to build ‘Indic’ AI models for critical sectors such as national security and defence, such models have not gained much traction in India (including on account of data scarcity in local languages), despite several Government-led initiatives. Nevertheless, such efforts continue, and recent achievements in this regard may be replicated in the future, including for the financial sector.

Further, AI models keep changing constantly and rapidly, evolving from the data generated in their source codes. With respect to the financial sector in particular, there might arise situations where the source code might not be updated by the back-end team with the latest financial and economic information, leading to incorrect output from the AI model.

In addition, the use of AI would require financial institutions to partner with third-party entities for the provision of technology-related services to operate and maintain AI systems. Such partnerships and/or contractual arrangements are likely to expose personal data to additional privacy risks, potential breaches, and/or instances of unauthorized processing, including with respect to certain sensitive information relating to customers. In this regard, for an overview of India’s new data protection regime comprising the Digital Personal Data Protection Act, 2023 (“DPDP Act“) and its draft rules, see our notes here and here. For a discussion on contractual arrangements with third-party entities for data processing, see our note here. Further, cybersecurity concerns may be exacerbated through manipulations of training datasets, leading to erroneous decision-making.

Finally, there are ethical and economic concerns involved in unrestrained AI dependence. AI models in finance might be biased towards or against a specific group or type of customers, including those with a certain profile, background, other demographic features, and/or income range. In such situations, decisions made pursuant to ill-fitted assumptions might lead to unreasonable discrimination and even financial loss.

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