Synopsis: Fraud losses have ballooned to Rs 36,014 crore as Mule accounts pose a serious threat and are everywhere, and cyber scams just keep growing. Now, RBI, which brought in MuleHunter.AI earlier, is quickly becoming a serious shield for India’s digital banks.
India’s digital economy has grown rapidly in recent years. Millions of people now use online banking, mobile wallets, the Unified Payments Interface (UPI), and digital platforms for their daily financial transactions. Along with this, the growth of mule accounts and cyber fraud also increased and has made it much harder to detect and prevent financial crime using traditional methods.
As a result, the Reserve Bank of India (RBI) is turning more to AI-based tools like MuleHunter.AI to protect the financial ecosystem. And banks being one of the pillars of the economy, to strive forward needs serious technological advancements to curb this threat.
Recently, India’s home minister, Amit Shah, emphasised the need to tackle the rising issue of mule accounts during a meeting. He stated that the banking sector should use the Mule Hunter software developed by the Government of India and the RBI to clean up its systems.
Mule accounts are bank accounts that criminals exploit to transfer stolen or illegal money. These accounts may be opened with fake documents, or criminals may trick real people into allowing them to use their accounts. The money first enters the mule account and is quickly moved elsewhere to obscure its source. This makes tracking the actual criminals extremely difficult for banks and law enforcement. In simpler terms, a mule account acts as a middleman, helping criminals conceal illicit funds.
Digital Payments industry
India’s digital payment ecosystem has witnessed strong growth in transaction value. In FY24, the total value of digital transactions stood at around Rs 231 lakh crore, which increased to nearly Rs 300 lakh crore in FY25, reflecting nearly 30% year-on-year growth. Additionally, this is expected to grow by a staggering CAGR of 25 percent over the next five years.
India’s digital payment transaction volume has also grown sharply. In FY24, total digital transactions stood at about 150 billion, which jumped to 206 billion in FY25, marking a strong 37% year-on-year increase. Additionally, this is expected to grow by a staggering CAGR of 24 percent over the next five years.
The Growing Problem
The statistics illustrate a troubling trend. In FY 2024-25, the total amount involved in banking frauds almost tripled to about Rs 36,014 crore, up sharply from around Rs 12,230 crore the previous year, even though the number of reported fraud cases fell from 36,060 in FY24 to 23,953 in FY25.
This unusual trend suggests that while fewer frauds were detected, the value of those frauds was much higher, driven by large-scale scams. One reason for this increase is the reclassification of older, significant cases, but it also points to a growing financial risk.
Other reports indicate that in 2025, people across India lost nearly Rs 19,813 crore due to fraud and cheating complaints, spread across over 21 lakh reported cases. This figure is significantly higher than in previous years and includes various digital fraud schemes, showing that public exposure to online scams is rising.
Cybercrime data underscores this increase: national reporting systems recorded 36 lakh cyber fraud cases in 2024, leading to losses exceeding Rs 22,845 crore. This marks a substantial rise from the prior year and resulted in more than 10,000 arrests. These numbers show that digital fraud is not just about stolen data; it is leading to significant financial losses for individuals and organisations.
On a more detailed level, reports from major banks highlight growing threats. For instance, India’s largest PSU bank, SBI, recorded nearly 16,000 cyber fraud cases over 22 months, resulting in losses exceeding Rs 118 crore. This shows that individual banks are battling a constant wave of digital fraud, with online banking, ATM, and mobile fraud significantly contributing to losses.
Additionally, the number of mule accounts discovered reflects how widespread the issue has become. According to law enforcement data, cybercrime investigations uncovered over 8.5 lakh mule accounts used by fraud networks across numerous bank branches to channel illegal funds. This deepens the need for better detection tools. According to the National Crime Records Bureau (NCRB), online financial frauds account for 67.8 per cent of cybercrime complaints
Why Traditional Methods Fall Short
Until recently, most banks depended on rule-based systems to identify unusual activities, such as transactions exceeding specific limits or transfers to various accounts within a short period. While these methods were useful, they were inflexible and easily circumvented by criminals who could break money into smaller transactions or use complex layering strategies. Consequently, many mule accounts went unnoticed until significant losses occurred.
Moreover, fraudsters have become more sophisticated, employing tactics such as phishing, fake digital “arrest” scams, impersonation, and engineered messages to deceive unsuspecting users into sharing sensitive information or moving money. Industry reports show that phishing alone accounts for a large portion of fintech fraud cases, with fraudsters often impersonating bank or government officials.
As digital fraud continues to evolve, merely tightening rules or adding basic alerts is insufficient. This is where advanced artificial intelligence, capable of recognising patterns that humans may not see, becomes crucial.
Enter MuleHunter.AI
Recognising the limitations of traditional systems, the RBI developed MuleHunter.AI through the Reserve Bank Innovation Hub. This AI and machine learning-based model is designed to detect mule accounts more effectively. Rather than relying on binary rules, the system analyzes behavior patterns, transaction flows, and account relationships across millions of data points.
Experts studied 19 different patterns commonly associated with mule accounts and trained the AI to recognise them automatically. During its starting phase, two large PSUs have pilot tested it and have seen significant results through this tool.
This machine-learning technology learns from actual data: every time it scans transactions and accurately identifies suspicious behaviour, it enhances its detection capabilities. In pilot projects with large banks, MuleHunter.AI showed much greater accuracy and speed than previous methods, flagging suspicious accounts sooner and reducing the likelihood of fraud escalating into major losses.
The AI tool is also designed for collaboration. Over 20 banks and financial institutions have already started using or testing the system. As more banks adopt it and share anonymised data, MuleHunter.AI becomes more robust and adaptable, helping banks learn from each other’s experiences and apply lessons in real time. This collective intelligence strengthens the entire banking system against fraud.
Real Impact and Future Potential
The launch of MuleHunter.AI comes at a time when regulators and law enforcement call for a zero-fraud future. The RBI and government agencies have initiated awareness campaigns, improved cybercrime reporting systems, and urged banks to enhance onboarding and monitoring processes. Together with advanced tools like MuleHunter.AI, these efforts aim to significantly cut the number and value of fraud cases.
Early signs are encouraging. By identifying mule accounts early, banks can step in before money leaves the system, safeguarding both customers and institutions. Tools like the Financial Fraud Risk Indicator (FRI) have already helped prevent losses of hundreds of crores in cyber fraud during their initial months of use, demonstrating how technology can complement regulation to protect users.
Looking ahead, countries around the world are observing India’s strategy. If MuleHunter.AI continues to advance and gets embraced by more financial institutions, including non-bank payment systems, it could establish a global benchmark for proactive fraud detection. For everyday users, this translates into safer digital payments, fewer losses, and greater confidence in online banking.
Hence, in conclusion, India’s digital financial ecosystem is vast and continually growing. Along with this growth comes the risk of fraud, but the digital revolution also provides the means to combat it.
Traditional methods, while necessary, are no longer adequate to stop sophisticated fraud networks using mule accounts and layered scams. By implementing a system like MuleHunter.AI, capable of learning, adapting, and recognising patterns that humans may struggle to see, the RBI is enhancing the banking system’s defences. Given the rise in fraud numbers and increasing financial losses, this AI-driven tool is quickly becoming essential in India’s efforts to build a safer, more secure digital economy.
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