Shah announced that the Indian Cyber Crime Coordination Center (I4C), a specialized agency that fights cyber crimes, has signed a memorandum of understanding with the Reserve Bank of India’s Innovation Hub to leverage AI to fight cyber fraud.
In a post on Combating deception. This move will feed data from I4C’s suspect registry to an AI-driven fraud detection system that will quickly detect and weed out hidden mule accounts to serve the public’s next-generation shield against cybercrime. ”
Also read: RBI policy has a big impact on bank fraud: RBI’s new AI tool MuleHunter.ai helps reduce digital fraud
Ranjeet Bellary, Partner, EY Forensic and Integrity Services – Cyber Forensics, told ET Wealth Online that this MoU between I4C and RBIH will strengthen collaboration between law enforcement and the banking ecosystem, enabling rapid sharing of intelligence and deployment of AI tools to detect and stop fraud.
According to Bellary, this means that suspicious accounts, particularly mule bank accounts, can be identified and blocked earlier, reducing the likelihood of individuals suffering large-scale financial losses.
Bellary said the key impact for Indian consumers will be improved security of digital transactions across UPI, online banking and fintech platforms. Faster detection of fraudulent funds, faster freezing and a more integrated response system will increase the likelihood of fraud prevention and recovery of funds, while also increasing overall confidence in India’s digital financial ecosystem.
Also read: Plans underway to capture ‘mule’ accounts in real time
What is a money mule scam?
Tarun Wig, co-founder and CEO of Innefu Labs, told ET Wealth Online that money mule fraud is simply the use of someone else’s bank account to launder funds, usually obtained through cyber fraud or other anti-social activities.
“In most cases, cybercriminals use the accounts of innocent individuals to quickly move stolen funds through multiple banking channels, making it difficult for authorities to track down the original fraudsters,” Wigg said.
Sometimes, innocent individuals who are in dire need of money “lend” the use of their bank accounts to cyber criminals and anti-social elements so that they can use them as money mules to launder funds.
“Scammers often target students, the unemployed, gig workers, or digitally naive citizens, promising fees for allowing transactions through their bank accounts in order to use them as pass-through vehicles for money laundering,” Wigg said. For example, a person may receive a message offering a simple “work from home” income and be asked to take the money into their account and send it elsewhere while keeping a small percentage.
How can AI help fight money mule banking fraud?
Experts say AI can help here because traditional systems cannot cope with cybercriminals and anti-social elements who typically move funds between multiple money mule accounts, sometimes within minutes, to avoid detection.
Ranjeeth Bellary, partner at EY Forensic and Integrity Services – Cyber Forensics, told ET Wealth Online that artificial intelligence (AI) is becoming central to the fight against cyber fraud as modern cyber crimes are being committed at a scale and speed that manual monitoring cannot match.
According to Bellary, AI can help analyze large amounts of banking and transaction data in real-time to detect suspicious patterns that indicate fraud.
“We can identify anomalies such as sudden spikes in activity, unusual trading behavior, accounts being used to quickly move funds across multiple channels, and common signs of mule accounts used by cybercriminals,” Bellary said.
Unlike traditional rules-based systems, AI continuously learns and adapts to evolving fraud tactics.
It also enables network-level analysis and linking of accounts, devices, and transaction trails to uncover organized fraud groups rather than individual incidents.
“This will allow authorities and banks to intervene more quickly, provide early warning of risky transactions and freeze funds before they are siphoned off, thereby moving the system from reactive investigation to proactive prevention,” Bellary said.
Essentially, AI moves cyber fraud prevention from a reactive model (“investigating fraud after it happens”) to a predictive preventive model (“identifying risks before money is gone”).
How will this new memorandum help Indians fight cyber fraud?
The memorandum of understanding between I4C and RBIH is more important than it appears on the surface, according to Wig. At its core, it’s about bridging a gap that has been exploited for years: the disconnect between those who track cybercriminals and those who actually control the financial infrastructure they exploit.
A practical highlight, according to Wig, is incorporating I4C’s Suspect Registry into tools like MuleHunter.ai.
Wigg said banks have historically been reactive: fraud is reported, an investigation begins, and the money is long gone. Feeding live suspect intelligence into AI-powered detection reverses that order. Mule accounts are flagged early, sometimes before a single rupee has passed.
“For everyday users, this means increased odds against all kinds of digital fraud, UPI fraud, phishing, fake investment schemes, and account takeovers. These are no longer edge cases,” Wigg said. They occur on a large scale, and the speed with which stolen funds are distributed across mule networks makes manual tracking nearly impossible.
Equally important is the layer of coordination this creates between banks, regulators and law enforcement. Cybercrime in India today is systemic and systemic and cannot be addressed with piecemeal responses.
“A shared intelligence framework moves us from reactive firefighting to a true preventative response,” said Wigg.
