The Promise of Digital Inclusion
For years, the goal of financial inclusion in India has been to bring banking and credit to every corner of the country. AI and machine learning have been positioned as powerful tools to achieve this. Financial institutions are using AI to analyse alternative
data points—like utility bill payments or digital transaction histories—to assess the creditworthiness of people without a formal credit history. This has the potential to unlock formal credit for millions of 'thin-file' borrowers, such as gig economy workers or small merchants, who were previously invisible to the traditional banking system. The appeal for banks and fintech companies is clear: AI models can process vast amounts of data to make faster, more consistent decisions, reduce operational costs, and manage risk more effectively. For customers, this can mean quicker loan approvals, more personalised services, and easier access to financial products through their smartphones.
The Emerging Risk of Algorithmic Barriers
However, the same technology that promises to open doors can also create new, invisible walls. The core of the current debate revolves around the risk of algorithmic bias. An AI model is only as good as the data it's trained on, and if that data reflects existing societal inequalities, the algorithm can learn and even amplify them. For example, a model might inadvertently discriminate against loan applicants from a particular region, demographic, or gender simply based on patterns in historical data. This can lead to deserving individuals being denied credit for reasons they can't understand or contest. Beyond bias, there's the challenge of the digital divide. An over-reliance on AI-driven services risks excluding those who are not digitally literate, particularly the elderly or those in rural areas with limited connectivity. When a loan application is rejected by an algorithm and the only recourse is to interact with a chatbot, customer access can feel more restrictive, not less.
Regulators Walk a Tightrope
Recognising both the potential and the perils, Indian regulators are stepping in. The Reserve Bank of India (RBI) and the Securities and Exchange Board of India (SEBI) are moving towards creating comprehensive guidelines for the use of AI in finance. Recent draft proposals from the RBI, issued in June 2026, emphasise the need for robust model risk management frameworks. These proposals call for banks and financial institutions to ensure human oversight of AI systems, conduct independent validations of models to check for fairness and bias, and maintain clear 'explainability' standards for automated decisions. Crucially, the RBI has suggested that customers interacting with an AI must be informed and given the option to switch to a human agent. Similarly, SEBI has been focusing on responsible AI usage, proposing guidelines around governance, investor protection, and disclosure for AI tools used in the securities market. The regulatory approach isn't to stifle innovation but to build guardrails that ensure AI is used responsibly and ethically.
What It Means for the Everyday Customer
Ultimately, this debate is about the future of every Indian's relationship with their finances. The outcome will determine whether AI becomes a genuine force for democratising finance or a tool that deepens existing divides. The push for 'explainable AI' means that in the future, if an algorithm denies you a loan, the bank should be able to tell you why in simple terms. The requirement for human oversight provides an essential safety net, ensuring that a computer's decision isn't final and that customers have recourse. The focus on fairness and bias audits aims to build a more equitable system where your access to financial services depends on your financial behaviour, not your postcode or background. While the technology offers immense benefits, the ongoing regulatory discussions show a growing consensus that innovation cannot come at the cost of transparency, fairness, and the fundamental right of a customer to be heard and understood.
