India's fintech industry is experiencing a fundamental shift as AI moves from experimental tools to core operational infrastructure. According to Business Standard, fintech firms are now embedding AI into the core operating layers of their businesses across underwriting, fraud detection, KYC verification, collections, customer support, compliance monitoring, engineering workflows, and internal productivity systems. This transformation comes as India processed more than 22.64 billion UPI transactions amounting to a record ₹29.52 trillion in March 2026, making manual monitoring increasingly impractical. Rohit Mahajan, founder and CEO of Plutos ONE, explains that AI has moved beyond experimentation and is now deeply embedded into operational and technology frameworks across compliance, onboarding workflows, operational support, and productivity management. The shift is driven by digital transaction volumes surging, fraud patterns becoming more sophisticated, and regulators tightening oversight while fintech firms face growing pressure to improve profitability without proportionately increasing headcount.
Generative AI is enabling more sophisticated fraud schemes including personalized phishing attacks, synthetic identities, and deepfake-enabled scams that traditional systems struggle to detect. A recent Experian-Forrester study found 69% of Indian organizations believe existing KYC systems cannot adequately detect AI-generated fake documents, while 65% identified GenAI as the biggest fraud threat they face. At PayU, AI is increasingly driving the first layer of fraud monitoring, with internal AI systems handling continuous monitoring, pattern recognition, and threat prioritisation across millions of data points. Manas Mishra, chief product officer at PayU and Wibmo, notes that payment fraud moves at machine speed, requiring AI-led real-time monitoring while maintaining human expertise for governance, investigations, and complex escalations. Industry experts believe the future model will increasingly become AI-led and human-governed rather than fully automated, addressing the growing sophistication of cyber threats targeting India's critical digital infrastructure.
AI adoption across banks and Non-Banking Financial Companies (NBFCs) has shifted from experimental pilots to a core infrastructure layer, driven by the need to slash secured lending timelines. While a typical secured loan file takes 18 to 21 days to move from sourcing to sanction, the bulk of this delay stems from administrative coordination rather than underwriting. Rajat Deshpande, CEO and Co-Founder of credit infrastructure platform FinBox, said the sector is experiencing its sharpest technology pull at the origination stage. Lenders are aggressively deploying AI to eliminate the "file assembly tax"—the hour credit managers waste organizing raw documents—and to fix the "bounce-back loop," where files shuffle between relationship managers and operations teams multiple times due to missing data. FinBox has already deployed its Atlas platform across five financial institutions, with advanced modules for credit appraisal, fraud detection and institutional configuration currently in active pipeline development.
Traditional digital applications rely heavily on forms written in English, but over half of India's small businesses operate in tier-2 cities and rural areas where these assumptions fail. The platform adopts conversational onboarding to remove language barriers entirely, allowing users to interact in their native dialect using voice, video, or chat. FinBox's conversational AI platform works within the clients' ecosystem to ensure data confidentiality while enabling formal credit systems to reach an entirely new demographic of borrowers. The true value of conversational AI lies in its ability to translate informal inputs into structured data - while a borrower shares business details via casual voice message or chat, the AI works in the background to extract relevant financial metrics and personal data, organizing raw inputs into a clean, decision-ready file for underwriters. As digital lending scales, priorities are moving from fixing user interfaces to removing forms entirely, making credit accessible to tier-II markets and small businesses.
The funding slowdown and growing scrutiny from public markets are reshaping how fintech firms approach AI investments, prompting companies to focus on measurable operational efficiencies, faster execution, and lower servicing costs. Gaurav Gupta, senior vice-president at Payoneer, reports that AI integration is deepest across product, research and development, customer support, compliance, and growth operations. Raman Khanduja, CEO of Mintoak, notes that many companies are still in the investment phase, seeing initial increases in costs as they invest in AI capabilities to meet rising customer expectations. However, industry experts caution against viewing AI purely as a cost-cutting tool, with Vijay Mani from Deloitte India stating that it is too early to say conclusively that AI has meaningfully reduced operational costs across the board, though there is strong evidence of cost reduction in areas where AI has moved beyond experimentation. Raman Khanduja emphasizes that the focus is on driving adoption, improving productivity, and identifying high-impact use cases rather than immediate cost reduction.
Despite rapid AI adoption, executives and analysts said fintech firms continue to face important governance and accountability challenges around explainability, particularly in lending decisions. Manas Mishra from PayU emphasizes that responsible automation matters more than full automation, with areas involving money movement, regulatory compliance, sensitive data, and customer protection requiring human oversight. Vijay Mani from Deloitte India warns that the real impact of AI would come only when companies redesign processes from an AI-native perspective, balancing governance, accountability, and regulatory considerations. Analysts say AI could eventually reshape competitive dynamics within India's fintech industry, with larger platforms better positioned to deploy AI at scale potentially widening the gap with smaller firms, while focused players with strong use cases can still compete by leveraging AI in targeted, high-impact ways. Deloitte's Mani concludes that ultimately, the fundamental business model matters more than AI alone, emphasizing that applying AI to weak business models will not provide meaningful results.
