Today, artificial intelligence (AI) is increasingly becoming integral to various sectors of India’s economy, from agriculture and healthcare to logistics, energy, retail, and governance. The rapid transformation is reflected in the nation’s growing AI market, which generated approximately ₹1.34 lakh crore in revenue in 2024 and is projected to expand significantly. A commissioned report anticipates that AI adoption could yield up to ₹33.8 lakh crore in economic value for India by 2030.
Globally, nations such as the European Union, China, and Brazil have put into place AI-specific regulations emphasizing fairness, transparency, and accountability. These themes are gaining traction within India’s evolving digital finance landscape as well.
Building the Foundation: Regulation and Policy Momentum
India is establishing robust policy frameworks to support ethical and inclusive AI adoption. During the Global Fintech Festival 2025, Prime Minister Narendra Modi stated that AI in India should be “all-inclusive” and based on a national “trust layer” designed to protect data and privacy. Initiatives like the IndiaAI Mission, the AI Safety Institute, and proposed multi-stakeholder oversight bodies aim to foster innovation within regulated boundaries. The Reserve Bank of India’s FREE-AI framework (Framework for Responsible and Ethical Enablement of AI) is set to guide financial institutions on governance, fairness, and transparency. These efforts align with the Digital Personal Data Protection Act, showcasing India’s commitment to responsible and trust-based AI.
In the financial services sector, particularly lending, AI is revolutionizing underwriting, risk scoring, fraud detection, collections, and customer service. Lending decisions that were once manual and time-consuming are becoming automated and real-time, driving efficiency within the financial ecosystem. For non-banking financial companies (NBFCs), such regulatory measures reduce uncertainty and set clear expectations as they scale AI capabilities.
The Ethical AI Advantage for NBFCs
Ethical AI serves not merely as a compliance requirement but as a strategic differentiator for NBFCs.
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Explainable Credit Decisions Ethical AI systems should convey credit decisions with clarity, focusing on measurable factors such as cash flow, repayment history, or collateral. For example, a small-fleet operator in Trichy applying for a top-up loan could receive an AI-generated summary detailing his eligibility based on consistent EMI payments and a rise in vehicle utilization. This transparency aids in building trust.
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Bias-Free Underwriting & Inclusive Growth Women-led kirana stores in Tier-3 towns often face difficulties as thin-file borrowers. By utilizing alternative data such as POS transactions and supplier payments, NBFCs can identify reliable entrepreneurs previously overlooked due to conventional credit scoring. Regular audits and fairness checks help expand access to underrepresented groups, promoting inclusivity.
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Fair Pricing Through Responsible Data AI-based pricing should balance personalization with fairness. Ethical frameworks can reward positive behaviors, ensuring that lenders maintain equitable practices. For instance, an MSME with a solid repayment record could qualify for a lower renewal rate, with clear explanations provided regarding how repayment behavior influenced this benefit.
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Human-Centric Trust & Transparency As technology evolves, it should enhance human interaction rather than replace it. Features like vernacular dashboards and simplified communication pathways ensure that AI remains approachable for all customers. For example, a commercial driver could use a local-language interface to manage loan details, with seamless access to support in his preferred language, fostering empathy within an AI environment.
The Way Forward for NBFCs
To effectively implement ethical AI, NBFCs should adopt a disciplined, governance-driven approach:
- Pilot First, Scale Later: Testing AI credit models in select branches lets NBFCs compare AI outcomes with human decisions, identifying biases or data gaps early.
- Strong Governance: An AI committee comprising members from various departments can quarterly review model outcomes to ensure fairness and equitable lending practices.
- Privacy by Design: When training AI models, NBFCs should anonymize personal data, focusing on summarized data, ensuring privacy while maintaining functionality.
- Explainability Tools: AI systems should provide transparency regarding credit decisions by highlighting key contributing factors, reinforcing confidence among borrowers and regulators.
- Continuous Monitoring: Regular dashboards should track metrics such as model drift and fairness to catch deviations early, enabling timely adjustments.
- Customer Education: Utilizing multilingual educational materials can illuminate AI’s benefits for borrowers.
By aligning AI innovation with ethical practices, NBFCs can cultivate genuine trust and lead in responsible digital lending, contributing to a fairer and more inclusive financial landscape.
