Vishleshan for Regulatory Exams, 21st August 2025: GM Crops Block Indo-US FTA & AI Finance Framework Needed

Want to get ready for the UPSC, RBI, SEBI, or NABARD exam? If yes, you have to stay updated about important economic and regulatory updates. In today’s edition of Vishleshan, we’ll discuss all about the GM crops blocking the Indo-US FTA & why an AI finance framework is needed. These issues are highly relevant for all the upcoming competitive exams mentioned above. Keep reading to stay ahead with a clear understanding of these current updates.

Soy Much Drama: GM Crops Blocking the Indo-US FTA

Context: For Indian farmers, soy and corn are major crops grown on 13 million hectares and 12 million hectares, respectively. While US has been pressing India to open up imports of genetically modified crops such as corn and soybean. Can India allow imports when its own farmers are denied access to transgenic technology?

Link to the Article: Mint

The United States is actively pressuring India to open its markets to imports of genetically modified (GM) crops, specifically corn and soybean. This pressure comes as US exports of these commodities are sliding due to reduced purchases from China. India, however, is reluctant to open its markets to these imports, citing a significant competitive disadvantage for its own farmers who are denied access to the same GM technology. This situation highlights India’s ongoing policy confusion and internal debate surrounding transgenic technology, even as the nation grapples with low agricultural productivity and a growing dependence on imports.

Genetically Modified (GM) Crops:

• Definition: Genetically Modified (GM) crops, also known as transgenic crops, are plants whose genetic material has been altered using genetic engineering techniques. This alteration gives them new traits, such as resistance to pests, tolerance to herbicides, or enhanced nutritional value.
• Need for GM Crops:
  • Food Security and Malnutrition: With a growing global population, GM crops can offer increased yields and enhanced nutritional content, helping to address food security and malnutrition. GM crops can boost yields.
  • Pest and Herbicide Resistance: GM crops with transgenic traits can offer protection from pests and tolerance to herbicides, which can lead to higher yields by reducing crop loss and competition from weeds.
  • Climate Resilience: GM crops can be engineered to be more resilient to environmental stressors like drought, heat, and salinity, which is increasingly important in the face of climate change.
  • Other Reasons: GM technology can also improve a crop’s shelf life, lower production costs, and lead to more efficient farming practices.
• Global Achievements in GM Technology:
  • Many countries, particularly the US, have widely adopted GM crops. The US has achieved high yields, with soy yields at 3.5 tonnes per hectare and corn yields at over 11 tonnes per hectare.
  • This high productivity is attributed to planting GM crops with traits like pest resistance and herbicide tolerance, using improved varieties of seeds, and employing precision farming techniques.
• India’s Progress with GM Crops and Milestones:
  • Approved GM Crop: The only GM crop allowed in India is cotton, a fibre crop. Known as “Bt cotton”. This variety was approved in 2002 by the Genetic Engineering Appraisal Committee (GEAC) of the Ministry of Environment, Forest and Climate Change.
  • Milestones:
    • In 2022, India’s biotech regulator approved the environmental release of GM mustard (DMH-11), developed by Delhi University. However, its commercial release has been stalled due to an ongoing case in the Supreme Court.
    • Field trials of GM corn are being carried out by Punjab Agricultural University.
    • Farmers in Gujarat and Maharashtra are growing unapproved herbicide-tolerant GM cotton due to the declining yields of the older approved GM technology.
• India’s Concerns with Foreign GM Imports:
  • Unfair Competition: India is reluctant to allow GM imports when its own farmers are not allowed to use the technology. This would create an unfair competitive environment, given the vast difference in yields.
  • Impact on Domestic Producers: Allowing imports, especially for commodities like corn, would put Indian farmers at a disadvantage and could hurt their profitability.
  • Net Exporter of Soy Meal: India is a net exporter of soy meal (de-oiled cake), and importing whole soybeans (from which oil is extracted, leaving the meal as a byproduct) could disrupt this trade balance.

Analysis of the Article: Decoding the US Pressure on India

The article decodes the US’s renewed push for GM crop imports into India, analysing the reasons behind the pressure and explaining India’s resistance, which stems from a mix of policy confusion, domestic concerns, and a competitive disadvantage.

1. The US’s Motivation to Sell GM Crops to India:

• Sliding Exports: US exports of corn and soybean are sliding because China, its largest buyer, is buying less.
• Data on US Exports:
  • Soybean exports fell from $27.4 billion in 2021 to $24.5 billion in 2024.
  • Corn exports were at $18.6 billion in 2021 and fell to $13.7 billion in 2024.
• Leveraging China’s Stance: China has been leveraging its position as a large buyer of US farm commodities (and raising purchases from Brazil) to secure favourable tariffs.
• India as a Potential Market: Given that India is a large importer of edible oils and has a growing demand for corn to produce ethanol, the US sees India as a potential market to offload its surplus GM corn and soybean.

2. India’s Reluctance and Policy Confusion:

• Dismal Productivity: India’s reluctance stems from the “dismal productivity” of its domestic crops, which makes them uncompetitive. Indian soy yields are 1.2 tonnes per hectare, and corn yields are 3.5 tonnes per hectare, while US yields are about three times more.
• Unfair Competition: It would be “unfair to make Indian farmers compete with their US counterparts” when Indian farmers are denied access to the same GM technology.
• Growing Demand for Corn: Indian farmers are increasingly shifting to corn due to soaring demand from multiple sectors and rising prices, making the government reluctant to allow imports that would undermine this trend.
• Industry Opposition: Sugar mills in India are opposed to importing GM maize for ethanol production, as it would reduce the demand for sugarcane, impacting the margins of the sugar industry and the profitability of cane growers.
• Weak Intellectual Property Rights: The article points out that weak IP protections in India make seed companies reluctant to introduce new GM technologies, creating a policy confusion that is taking a toll on productivity and increasing import dependence.

In conclusion, the US’s push for GM corn and soybean imports into India is a direct response to a decline in its exports, particularly to China. India’s reluctance, however, is a complex issue rooted in the dismal productivity of its own farmers, a lack of access to GM technology, and domestic concerns about unfair competition. This situation underscores the policy confusion surrounding transgenic technology in India, which is hindering agricultural growth and making the nation increasingly dependent on imports of commodities like soy oil.

Blackbox to Sandbox: Framework Needed for AI in Finance

Context: The Monetary Authority of Singapore (MAS) introduced “FEAT” (fairness, ethics, accountability, and transparency) principles in 2018. Examples show that trust and innovation can reinforce one another when regulators act early.

Link to the Article: Business Standard

The Reserve Bank of India’s (RBI) Committee on the Framework for Responsible and Ethical Enablement of Artificial Intelligence (FREE-AI) has released a comprehensive report proposing guidelines for the responsible use of AI in the financial sector. The framework, anchored around seven guiding principles or “sutras,” aims to foster innovation while mitigating risks. This initiative is crucial for India, where AI adoption is accelerating and holds the potential to transform everything from fraud detection to financial inclusion. However, without a strong governance framework, unchecked adoption could replicate existing biases, undermine trust, and create systemic vulnerabilities.

Artificial Intelligence (AI) and its Impact on the Banking and Financial System:

• What is AI?: Artificial Intelligence (AI) refers to the simulation of human intelligence processes by machines, particularly computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction.
• Positive Impacts of AI on Banking:
  • Enhanced Customer Engagement: AI can unlock new forms of customer engagement, including providing personalized services at scale. AI-powered chatbots and virtual assistants can provide 24/7 customer support, handling routine inquiries and freeing up human staff for more complex tasks.
  • Improved Credit Assessment: AI models can enable alternative and more accurate approaches to credit assessment by leveraging vast amounts of non-traditional data (e.g., utility bill payments, mobile usage patterns, e-commerce transactions). This can help expand credit access to “thin-file” or “new-to-credit” customers, particularly in underserved populations.
  • Advanced Fraud Detection: AI algorithms can improve risk monitoring and fraud detection by analysing patterns and anomalies in real-time. This can help identify suspicious transactions and enhance cybersecurity.
  • Operational Efficiency: AI can automate tedious, repetitive tasks, reducing manual errors and improving productivity. This allows human resources to focus on more strategic activities.
• Negative Impacts of AI on Banking:
  • Bias and Lack of Explainability: AI adoption could lead to new risks such as bias and lack of explainability. If AI models are trained on biased data, they can perpetuate discrimination, particularly in areas like credit scoring.
  • Data Protection and Cybersecurity: Increased AI adoption amplifies existing challenges related to data protection and cybersecurity. AI models themselves can be valuable targets for malicious actors.
  • Ethical Concerns: The use of AI raises ethical considerations regarding fairness, transparency, accountability, and the potential for job displacement.
  • High Implementation Costs: Implementing AI systems can require a substantial initial investment in infrastructure, technology, and skilled personnel.

RBI’s Committee on Responsible and Ethical AI (FREE-AI):

• Background and Formation:
  • The FREE-AI committee was constituted by the RBI in December 2024 to encourage the responsible and ethical adoption of AI in the financial sector.
  • It was a committee of eight experts and was chaired by Professor Pushpak Bhattacharyya of IIT Bombay.
  • Its mandate included assessing global and domestic AI adoption, evaluating regulatory approaches, identifying risks, and recommending a governance framework.
• RBI’s Plan for Action: The committee’s central recommendation is to treat innovation and risk mitigation as complementary objectives, guided by a set of seven core principles or “sutras”. The sutras are: trust, people first, innovation, fairness, accountability, explainability, and resilience. The framework is backed by 26 actionable recommendations across six pillars. These pillars are infrastructure, policy, capacity, governance, protection, and assurance.
• Recommendations for Fostering Innovation:
  • Shared Infrastructure: The committee recommends establishing a shared infrastructure to democratize access to data and computation. This would help build trustworthy AI models and reduce barriers for smaller players.
  • AI Innovation Sandbox: The report suggests creating an AI innovation sandbox, a controlled space for safe experimentation before deployment.
  • Indigenous AI Models: The report calls for developing indigenous, financial sector-specific AI models tailored for India’s needs. Off-the-shelf models, largely built on Western datasets, might overlook India’s diversity.
• Recommendations for Mitigating Risks:
  • Board-Approved AI Policy: Regulated entities are urged to formulate a board-approved AI policy that covers governance structure, accountability, risk appetite, and operational safeguards.
  • Expanded Product Approval: Product approval processes should be expanded to include AI-specific risk assessments before launching new financial products.
  • Enhanced Cybersecurity: The report recommends strengthening cybersecurity practices and incident reporting frameworks, and augmenting existing business continuity plans (BCPs) to include AI model-specific performance degradation.
  • Consumer Protection: It is crucial to update consumer protection frameworks and audits to include AI-related aspects and ensure that consumers are informed when they are interacting with AI.
• Other RBI & SEBI Initiatives:
  • RBI: Has developed an AI model called Mulehunter.ai to tackle mule accounts and a report suggests AI could improve banking operations by up to 46%.
  • SEBI: Has announced the use of AI to process IPO (Initial Public Offering) documents. SEBI has also issued a consultation paper proposing a regulatory framework for the responsible usage of AI/ML tools in the securities markets, emphasizing principles like transparency and accountability.

Blackbox and Sandbox:

• Blackbox: The term “blackbox” is used to describe an AI system where the internal workings and decision-making process are opaque and not easily understandable by humans. This lack of transparency and explainability is a significant risk, as it makes it difficult to detect bias, pinpoint errors, or hold the system accountable. The report seeks to move away from the “opacity of today’s AI ‘blackbox'”.
• Sandbox (Regulatory Sandbox): A regulatory sandbox is a controlled environment or framework set up by a regulator (like the RBI or SEBI) that allows businesses and innovators to test new products and technologies in a live, but limited, market setting, with a relaxed regulatory oversight. The FREE-AI committee proposes creating an AI “sandbox” for safe experimentation before deployment.
• Why They Are Important: The shift from a “blackbox” to a “sandbox” approach is crucial for AI adoption. It signals a move from a position of uncertainty and opaqueness, which can erode trust, to one of controlled experimentation and learning. The sandbox framework allows for the careful study of new AI solutions, their risks, and their unintended consequences before they are widely deployed, ensuring that innovation is fostered in a safe and responsible manner.

Analysis of the Article: Decoding the FREE-AI Framework

The article argues that the FREE-AI framework arrives at a critical juncture and is a necessary response to the rapid pace of AI adoption in India’s financial sector.

1. Critical Juncture and High Stakes:

• The global AI investment in finance is expected to jump from $35 billion in 2023 to $97 billion by 2027. This shows the rapid pace of growth and the critical need for a regulatory framework.
• In India, AI can transform everything from fraud detection to financial inclusion. However, the report cautions that unchecked adoption risks replicating biases, undermining trust, and exposing financial institutions to systemic vulnerabilities. The stakes are high, as poorly governed AI could entrench social biases and erode public trust.

2. Overcoming Barriers for Smaller Players:

• The report is particularly critical for smaller banks and non-banking financial companies (NBFCs), most of which report little AI adoption due to high costs, skill gaps, and poor data quality. Without support, only the largest banks may benefit, leaving smaller ones behind.
• The proposed common AI infrastructure, with pooled datasets and computing resources, is aimed at lowering these entry barriers and democratizing AI adoption.

3. Learning from Global Precedents:

• The article highlights that other global jurisdictions have already adopted similar proactive frameworks. The Monetary Authority of Singapore (MAS) introduced “FEAT” (fairness, ethics, accountability, and transparency) principles in 2018 and later launched a Veritas toolkit. Hong Kong’s Monetary Authority has a generative AI (GenAI) sandbox, and the UK’s Financial Conduct Authority offers a “supercharged sandbox”.
• These examples show that “trust and innovation can reinforce one another when regulators act early”.

4. The Real Test Lies in Execution:

• While the principles and report are a good beginning, the “real test lies in execution”. The RBI and financial institutions must now invest in capacity building, interoperability, and accountability structures.
• India’s digital public infrastructure, from Aadhaar to UPI, shows how inclusive design can create global benchmarks. The challenge is to replicate that success by moving from the “opacity of today’s AI ‘blackbox’ to tomorrow’s ‘sandbox’ experimentation”.

In conclusion, the FREE-AI report is a crucial and forward-looking document that provides a much-needed framework for India to responsibly and ethically enable AI in its financial sector. By emphasizing a unified vision built on principles of trust, fairness, and accountability, and by proposing a robust set of recommendations to foster innovation while mitigating risks, the RBI is positioning India to harness AI’s transformative power to drive growth, equity, and trust in the nation’s financial system.

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