The Silent Revolution in Your Wallet
Whether you realise it or not, Artificial Intelligence (AI) and Machine Learning (ML) are already deeply embedded in India's financial ecosystem. Every time you use UPI, receive a fraud alert, or apply for a quick digital loan, there’s a high chance an algorithm
is working behind the scenes. Financial institutions have rapidly adopted AI to automate processes, reduce costs, and enhance security. The most significant applications include real-time fraud detection, where AI systems monitor transaction patterns to flag suspicious activity, and algorithmic trading in the securities market. Another key area is customer service, with AI-powered chatbots handling queries and providing personalised banking recommendations. This technological shift is not just about convenience; it's fundamentally changing how financial services are delivered and managed across the country.
The Promise of Greater Inclusion
One of the most powerful arguments in favour of AI in finance is its potential to drive financial inclusion. Traditionally, getting a loan required a solid credit history, leaving out millions of first-time borrowers and those in the informal economy. AI-based credit scoring models are changing this by analysing alternative data points. With a user's consent, these algorithms can assess creditworthiness based on digital payment history, utility bill payments, and even GST filings for small businesses. This allows lenders to build a more holistic financial profile for individuals who were previously invisible to the formal credit system. Proponents argue this leads to faster, more accurate, and more inclusive lending decisions, potentially unlocking credit for a massive, underserved segment of India's population.
When the Algorithm Says 'No'
However, the rise of AI also brings significant risks. A primary concern is algorithmic bias. Since AI models are trained on historical data, they can inherit and even amplify existing societal biases, leading to discriminatory outcomes. An algorithm might, for instance, unfairly penalise applicants from a certain geographical area or those with specific spending patterns, even if those factors are not explicitly programmed. This is compounded by the "black box" problem, where the decision-making process of a complex AI is opaque and difficult to explain. If a loan application is rejected by an AI, the applicant may have no clear path to understand why or how to appeal the decision, raising serious questions about fairness and accountability.
India's Quest for a Rulebook
Recognising these challenges, India's financial regulators are stepping in. The Reserve Bank of India (RBI) and the Securities and Exchange Board of India (SEBI) are actively developing frameworks to govern the use of AI. In June 2026, the RBI released draft guidance on model risk management for banks and NBFCs. This framework makes it clear that financial institutions are fully accountable for the outcomes of their AI models, even those sourced from third-party vendors. Key proposals include mandatory board-approved risk management policies, independent validation of all models, and enhanced disclosure. Similarly, SEBI has been working on guidelines for AI in securities markets, focusing on investor protection and market integrity. Meanwhile, the Telecom Regulatory Authority of India (TRAI) has recommended a broader, cross-sectoral statutory body called the 'Artificial Intelligence and Data Authority of India' (AIDAI) to create a risk-based framework for AI use.
Keeping Humans in the Driver's Seat
A central theme emerging from the regulatory debate is the need for human oversight. The RBI's draft guidelines explicitly warn against "automation bias"—the tendency to overly trust automated outputs—and mandate meaningful human intervention. This includes requirements for a "human-in-the-loop" arrangement, override capabilities, and even "kill-switches" to halt malfunctioning AI systems. For customer-facing systems, the proposal requires disclosure that the interaction is with an AI and an option to switch to a human agent. The goal is not to stop innovation but to ensure that the final accountability for critical financial decisions rests with people, not just programs. This approach aims to build a system where AI serves as a powerful tool for analysis and assistance, but the ultimate judgment remains in human hands.
