How AI is becoming the operating backbone of India's fintech industry

AI is becoming part of the core infrastructure of India's fintech ecosystem as firms look to automate workflows, tackle fraud, and improve scalability

The first phase of AI adoption in India’s fintech industry revolved around customer-facing tools such as chatbots, recommendation engines, and marketing automation. Now, a deeper shift is unfolding.

 

Fintech firms are 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.

 

The shift comes as digital transaction volumes surge, fraud patterns become more sophisticated, regulators tighten oversight, and fintech firms face growing pressure to improve profitability while scaling operations without proportionately increasing headcount.

 

Industry executives say the shift is also being driven by the sheer scale of India’s digital finance ecosystem. In March, India processed more than 22.64 billion Unified Payments Interface (UPI) transactions amounting to a record ₹29.52 trillion, the highest ever in a single month, according to data from the National Payments Corporation of India (NPCI), making manual monitoring increasingly impractical. AI is therefore becoming less of an experimental technology and more of an operational necessity.

 

 

“At Plutos ONE, AI has clearly moved beyond the experimentation stage and is now becoming deeply embedded into our operational and technology framework,” said Rohit Mahajan, founder and chief executive officer at Plutos ONE, a Noida-based fintech startup.

 

Mahajan's company is integrating AI across compliance, onboarding workflows, operational support, complaint resolution and productivity management.

 

“For us, AI is no longer just a productivity tool. It is becoming a foundational layer for scaling fintech infrastructure, improving operational efficiency, and accelerating deployment,” he said. 

Fraud is becoming AI-enabled too 

Generative AI is enabling more personalised phishing attacks, synthetic identities, and deepfake-enabled scams that are harder to detect through traditional systems. A recent Experian-Forrester study found 69 per cent of Indian organisations believe existing KYC systems cannot adequately detect AI-generated fake documents, while 65 per cent identified GenAI as the biggest fraud threat they face.

 

At PayU, a diversified fintech platform, AI is increasingly driving the first layer of fraud monitoring. “Payment fraud moves at machine speed,” said Manas Mishra, chief product officer at PayU and Wibmo. “The real-time monitoring layer is increasingly AI-led, but human expertise remains critical for governance, investigations, regulatory judgement, and complex escalations.”

 

Mishra said PayU’s internal AI systems handle continuous monitoring, pattern recognition, and threat prioritisation across millions of data points, while human analysts focus on strategic investigations and complex cases.

 

Industry experts believe the future model across fintech will increasingly become AI-led and human-governed rather than fully automated.  

Profitability pressure is accelerating adoption 

The funding slowdown and growing scrutiny from public markets are also reshaping how fintech firms approach AI investments, prompting companies to look at measurable operational efficiencies, faster execution, and lower servicing costs.

 

Gaurav Gupta, senior vice-president and India site leader at Payoneer, a payment aggregator for cross-border transactions, said AI integration is deepest across product, research and development, customer support, compliance, and growth operations.

 

“A tool helps an individual complete a task faster. An execution layer changes how work gets done, with AI handling more of the workflow and humans supervising and making the final calls where needed,” Gupta said.

 

Several executives said AI is helping firms scale operationally without equivalent increases in manpower. However, industry experts caution against viewing AI purely as a cost-cutting tool.

 

“It is too early to say conclusively that AI has meaningfully reduced operational costs across the board,” said Vijay Mani, partner and banking and capital markets leader at Deloitte India. “However, there is strong evidence of cost reduction in areas where AI has moved beyond experimentation, especially as organisations become better at managing the cost of AI."

 

Kabeer Jain, cofounder and CTO at Mintoak, a platform facilitating digital payments for MSMEs, said many companies are still in the investment phase of AI adoption.

 

“Right now, the focus is on driving adoption, improving productivity, and identifying high-impact use cases,” Jain said. “Many organisations are seeing an initial increase in costs as they invest in AI capabilities to meet rising customer expectations.”  

AI moves deeper into lending, compliance, and customer operations 

According to industry executives and analysts, one of the strongest areas of AI deployment is lending and underwriting. Fintech lenders are increasingly using alternative data underwriting, behavioural scoring, early warning systems, and dynamic risk monitoring to improve credit assessment.

 

Industry executives say the future underwriting model is gradually shifting from static and periodic evaluations to continuous and real-time behavioural monitoring.

 

Another major area of adoption is customer support and grievance management. RBI Governor Sanjay Malhotra recently urged banks to adopt AI to improve customer grievance handling after commercial banks collectively received more than 10 million customer complaints in FY24.

 

Companies are increasingly deploying multilingual AI support systems, conversational interfaces, voice AI, and complaint analytics to improve response times and reduce manual intervention.

 

At PayU, AI-assisted systems now handle nearly 30-40 per cent of merchant queries at the first level, according to Mishra.

 

Executives also pointed to rising AI adoption inside engineering and product teams. “AI has clearly moved beyond experimentation into real workflows and production use cases,” Jain said. “The strongest adoption is in technology teams, where AI is deeply embedded into development and engineering processes.”

 

Companies are increasingly using AI coding copilots, automated testing systems, debugging assistants, and workflow automation tools to accelerate product development cycles.

 

Bruce Keith, chief executive officer and co-founder at InvestorAi, an AI-powered equity investment platform, said AI is also reshaping wealth management and investment distribution. “The rise of GenAI and agentic systems has helped large institutions improve targeting, follow-up, customer onboarding, and query handling,” Keith said.

 

Keith said Indian financial firms are currently using AI more aggressively on the distribution side to improve customer acquisition and servicing efficiency, because outcomes in these areas are easier to measure over shorter periods.

 

“The focus for most businesses is on distribution rather than manufacturing. How can I get more customers for less effort,” he said.

 

According to Keith, firms that build proprietary AI models rather than relying entirely on generic large language models could eventually create stronger differentiation in wealth and investment businesses. He added that AI-native entrants may disrupt parts of the traditional asset and wealth management ecosystem over the next few years. 

Governance concerns remain significant 

Despite rapid adoption, executives and analysts said fintech firms continue to face important governance and accountability challenges around AI deployment.

 

One major concern is explainability, particularly in lending decisions.

 

As underwriting models become more AI-driven, regulators may increasingly expect companies to explain why loans were approved or rejected.

 

Questions around bias, accountability, and hallucinations also remain significant, especially in regulated financial services environments involving customer money and sensitive personal data.

 

“At PayU, responsible automation matters more than full automation,” Mishra said. “Areas involving money movement, regulatory compliance, sensitive data, and customer protection must continue to have human oversight.”

 

Gupta of Payoneer said fintech companies must clearly define the boundary between supervised and unsupervised AI action.

 

“In a regulated industry, control is what makes velocity safe and sustainable,” Gupta said.

 

Deloitte’s Mani said the real impact of AI would come only when companies redesign processes from an AI-native perspective rather than simply layering AI over legacy systems.

 

“That transition will still need to be balanced with governance, accountability, and regulatory considerations,” he said.

 

The RBI has also been working on broader frameworks around responsible AI adoption in financial services. 

The bigger strategic question 

Analysts say AI could eventually reshape competitive dynamics within India’s fintech industry. Larger platforms with deeper access to data, capital, and infrastructure may be better positioned to deploy AI at scale, potentially widening the gap with smaller firms.

 

“At the same time, focused players with strong use cases can still compete by leveraging AI in targeted, high-impact ways,” Jain said.

 

Experts also caution that AI alone cannot compensate for weak business fundamentals.

 

“Ultimately, the fundamental business model matters more than AI alone,” said Deloitte’s Mani. “Applying AI to a weak business model will not help.”

 

For India’s fintech sector, it is becoming increasingly clear that AI is no longer confined to pilots or customer-facing experiments. It is becoming part of the infrastructure powering how financial platforms are built, monitored, and scaled.