How AI may become one of the most powerful tools in India's fight against financial fraud

Artificial intelligence could soon become one of the most important tools in India's fight against financial fraud as banks, regulators and technology firms accelerate investments in AI-powered systems capable of detecting suspicious transactions, identifying mule accounts and helping investigators sift through vast volumes of digital payment data.The growing focus on AI-led financial crime monitoring comes as global technology companies experiment with increasingly sophisticated AI agents that can investigate suspicious activity, gather evidence across multiple data sources and support compliance teams in anti-money laundering (AML) operations.

For India, the timing is significant.

The country's digital payments ecosystem continues to expand at breakneck speed. Unified Payments Interface (UPI) transactions touched 22.64 billion in March 2026, with a total value of Rs 29.53 lakh crore, according to National Payments Corporation of India data.

As transaction volumes soar, so does the challenge of detecting fraud in real time.Banks are already responding. Large lenders including HDFC Bank, ICICI Bank and State Bank of India have been expanding investments in fraud analytics, behavioural scoring systems and AI-powered transaction monitoring tools designed to identify suspicious activity before money leaves customer accounts."AI-led systems can materially reduce the compliance burden in financial frauds, and this continues to be one of the top areas of driving value from AI for banks," said Manoj Singodia, managing director and lead for financial services at Accenture India.

The push comes amid a sharp increase in financial crime. Bank frauds rose to Rs 36,014 crore in FY25 from Rs 12,230 crore a year earlier, according to Reserve Bank of India data. Separately, the Indian Cyber Crime Coordination Centre (I4C) reported around 24 lakh digital fraud complaints between April 2024 and January 2025.Industry executives say traditional rules-based fraud monitoring systems are increasingly struggling to cope with the scale and sophistication of modern scams."Banks are moving from static rules to real-time behavioural monitoring to detect mule accounts, anomalous payment patterns and suspicious networks earlier," said Ritwik Batabyal, chief technology and innovation officer at Mastek.

RBI's AI push

The RBI itself has become one of the strongest proponents of AI-assisted fraud detection. Through RBI Innovation Hub, the central bank developed MuleHunter.AI, a platform designed to identify fraudulent mule accounts in real time.

More than 15 banks are estimated to be using the system.Earlier this month, I4C signed an agreement with RBI Innovation Hub to integrate data from its suspect registry with MuleHunter.AI, allowing participating banks to identify potentially fraudulent accounts faster.The regulator's stance reflects a broader shift towards encouraging AI adoption while maintaining strict oversight. In 2025, the RBI introduced its Framework for Responsible and Ethical Enablement of Artificial Intelligence (FREE-AI), laying out governance, transparency and accountability requirements for AI deployments across the financial sector.That balance between innovation and oversight is likely to define how AI enters compliance functions."AI-driven AML systems offer banks substantial cost and time efficiencies given the magnitude of transactions processed daily by Indian banks," said Manish Chachada, co-founder and chief operating officer of cybersecurity research firm Cyble. However, he added that regulators are unlikely to hand complete investigative authority to autonomous systems.

Human oversight remains

Experts broadly agree that India's regulators will insist on keeping humans responsible for final decisions."Agentic AI systems can reduce investigation timelines from days to minutes," said Jaspreet Bindra, co-founder and chief executive of AI & Beyond. "But I do think RBI and FIU-India will mandate a strict human-in-the-loop framework."Batabyal echoed that view, describing AI as a "co-pilot" for compliance teams.

"Banks can use AI for detection, case enrichment and prioritisation, while final calls stay with compliance teams," he said.The next phase could involve AI agents performing much of the preliminary investigative work currently handled by compliance staff. Rather than merely flagging suspicious transactions, AI systems could automatically retrieve KYC records, analyse account relationships, cross-reference sanction lists and prepare draft reports for investigators."Agentic AI is reimagining slow and manual workflows, thereby improving speed, cost and accuracy," Singodia said. "Agents can triage alerts, unearth patterns and surface insights at scale with humans in the lead for decision making and accountability."Global AI companies are expected to see growing opportunities in the Indian market as banks look to modernise fraud monitoring infrastructure. However, experts say success will depend on adapting to local regulations and data sovereignty requirements."Global firms may find Indian partners, but only through controlled, regulator-aware deployments," Batabyal said. Localisation for UPI-scale transaction volumes, Indian-language context and compliance with data residency requirements will be critical.Bindra said international AI developers would likely find eager partners among banks and fintech firms, provided their systems are deployed within locally controlled infrastructure.Yet the growing use of AI in financial crime investigations is also raising fresh concerns around privacy and surveillance.

Real-time fraud monitoring

Fraud detection systems increasingly rely on analysing sensitive financial, behavioural and device-level data. As AI models gain access to broader datasets, questions are emerging over profiling, transparency and compliance with India's Digital Personal Data Protection (DPDP) Act."AI agents can develop a deeper understanding of the manner in which individuals conduct payments and banking transactions," Chachada said. That creates challenges around surveillance, transparency and data governance, he added.Bindra argued that AI systems used for fraud monitoring must provide auditable explanations for every decision. "It cannot be a black box," he said. "You need to provide the provenance and reasoning for flagging a transaction."The workforce implications are equally significant. While AI is expected to automate many repetitive compliance and KYC processes, industry executives believe the technology will transform jobs rather than eliminate them outright."Automation will inevitably reduce the need for repetitive entry-level roles in manual data gathering and basic KYC," Bindra said. At the same time, demand is likely to increase for model auditors, AI governance specialists, investigators and risk professionals capable of overseeing automated systems.