Union Home Minister, Amit Shah announced on May 12, 2026 that the Indian government has taken the bold step of using AI to combat money mule fraud especially since mule accounts are a big hurdle in curbing cyber crimes.
Shah announced that the Indian Cyber Crime Coordination Centre (I4C), a specialized agency designed to fight cybercrime has signed a MoU with the Reserve Bank of India’s Innovation Hub to use AI to combat cyber fraud.
Shah said in a post on X: “Modi govt is tirelessly working for cyber secure Bharat. Today, the I4C under MHA signed an MoU with Reserve Bank Innovation Hub (RBIH) unleashing the power of Artificial Intelligence to combat cyber fraud. The move will swiftly detect and cull hidden mule accounts by feeding the data from the I4C's Suspect Registry to the AI-driven fraud detection system and serve the citizens as their next gen shield against cyber crime.”
Ranjeet Bellary, Partner, EY Forensic and Integrity Services – Cyber Forensics said to ET Wealth Online that this MoU between I4C and RBIH strengthens coordination between law enforcement and the banking ecosystem, enabling faster sharing of intelligence and deployment of AI tools to detect and stop fraud.
According to Bellary this means suspicious accounts, especially mule bank accounts, can be identified and blocked earlier, reducing the chances of large-scale financial losses for individuals.
Bellary says that for Indian consumers, the key impact is safer digital transactions across UPI, online banking, and fintech platforms. Faster detection, quicker freezing of fraudulent funds, and a more integrated response system improve the likelihood of preventing fraud or recovering money, while also boosting overall trust in India’s digital financial ecosystem.
Also read: Plan in works to catch 'mule' accounts in real time
What is money mule fraud?
Tarun Wig, co-founder & CEO of Innefu Labs, said to ET Wealth Online that money mule fraud is simply using someone else’s bank account to launder money usually obtained from conducting cyber fraud and other anti-social activities.
Wig says: “In most cases, cybercriminals use innocent individuals’ accounts to move stolen money quickly across multiple banking channels, making it difficult for authorities to trace the original fraudsters.”
Sometimes, innocent individuals in dire need of money, ‘lend’ the usage of their bank accounts to cyber criminal or anti-social elements so that they can use them as money mules and launder the money.
Wig says: “Fraudsters often target students, unemployed individuals, gig workers, or digitally unaware citizens by promising commissions for allowing transactions through their bank accounts for using it as pass-through vehicles for money laundering.” For example, a person may receive a message offering easy “work from home” income where they are asked to receive money into their account and transfer it elsewhere while keeping a small percentage.
How can AI help in combating money mule banking fraud?
Cybercriminals and anti-social elements usually moves their funds between multiple money mule accounts sometimes within minutes to avoid detection and thus traditonal systems can't keep up, thus AI helps here, says experts.
Ranjeeth Bellary, Partner, EY Forensic and Integrity Services – Cyber Forensics said to ET Wealth Online that artificial intelligence (AI) is becoming central to the fight against cyber fraud because modern cybercrime operates at a scale and speed that manual monitoring simply cannot match.
According to Bellary, AI helps by analysing massive volumes of banking and transaction data in real time to detect suspicious patterns that indicate fraud.
Bellary says: “It can identify anomalies such as sudden spikes in activity, unusual transaction behaviour, or accounts being used to rapidly move money across multiple channels, common signs of mule accounts used by cybercriminals.”
Unlike traditional rule-based systems, AI continuously learns and adapts to evolving fraud tactics.
It also enables network-level analysis, linking accounts, devices, and transaction trails to uncover organised fraud rings rather than isolated incidents.
Bellary says: “This allows authorities and banks to intervene faster, flagging risky transactions early and helping freeze funds before they are siphoned off, thereby shifting the system from reactive investigation to proactive prevention.”
In essence, AI shifts cyber-fraud prevention from a reactive model (“investigate after fraud happens”) to a predictive and preventive model (“identify risk before money vanishes”).
How does this new MoU help Indians combat cyber fraud?
According to Wig, the MoU between I4C and RBIH matters more than it might appear on the surface. At its core, it's about closing a gap that's been exploited for years, the disconnect between who tracks cybercriminals and who actually controls the financial infrastructure they abuse.
Wig says that the practical centrepiece is plugging I4C's Suspect Registry into tools like MuleHunter.ai.
According to Wig, banks have historically been reactive, fraud gets reported, investigations begin, money is long gone. Feeding live suspect intelligence into AI-powered detection flips that sequence. Mule accounts get flagged earlier, sometimes before a single rupee moves through them.
Wig says: “For everyday users, this means better odds against the full range of digital fraud, UPI scams, phishing, fake investment schemes, account takeovers. These aren't edge cases anymore.” They're happening at scale, and the speed at which stolen funds get dispersed across mule networks makes manual tracking nearly impossible.
What's equally important is the coordination layer this builds between banks, regulators, and law enforcement. Cybercrime in India today is organised and cross-institutional, a fragmented response simply doesn't cut it.
Wig says: “A shared intelligence framework changes the response from reactive firefighting to something closer to actual prevention.”
