India’s fight against digital financial fraud is moving deeper into the banking infrastructure itself. The Reserve Bank Innovation Hub (RBIH), a wholly owned subsidiary of the Reserve Bank of India, has developed MuleHunter.ai, an AI and machine-learning system designed to identify bank accounts that may be acting as “mule accounts” for cybercriminals.
The idea is important because many digital frauds do not end with the victim sending money directly to the criminal. The money often moves through accounts belonging to people who knowingly or unknowingly allow their bank accounts to be used as an intermediary. Those accounts can then become part of a chain of transfers designed to obscure where the money came from and where it ultimately goes.
RBIH’s own documentation describes MuleHunter as a supervised machine-learning system intended to identify such accounts with higher accuracy and fewer false positives than traditional rule-based systems. The government said in March 2026 that the solution was already live in 26 banks and being scaled further. RBIH’s technical documentation also says the model is designed to integrate with banks’ existing Enterprise Fraud Risk Management and anti-money-laundering systems.
The real problem is the account behind the fraud
A digital scam can happen in seconds, but tracing the money afterwards is much harder. Fraudsters may use several accounts, rapid transfers, sudden changes in transaction behaviour and networks of intermediaries before the funds are withdrawn or moved again.
That is where mule accounts become important. RBIH describes them as accounts used to transfer and obscure illicit funds, making it harder for banks and investigators to identify the actual beneficiaries of financial crime. Traditional systems often depend on predefined rules and thresholds. Those rules can work for known patterns, but criminals can change their behaviour faster than a bank can rewrite every rule.
MuleHunter takes a different approach. RBIH says the system analyses transaction patterns and learns characteristics associated with mule activity. Its public project description points to signals such as repeated high-velocity transactions and sudden spikes in activity across large volumes of transactions.
Why AI matters when fraud patterns keep changing
The advantage of machine learning in this context is not that it magically knows which customer is a criminal. Its value is in finding combinations of behaviour that may be difficult to capture through a simple rule.
For example, an account that suddenly receives money from many unrelated sources, moves much of it onward in a short period and then shows another unusual burst of activity may deserve a different risk assessment from an account with a stable transaction history. A machine-learning model can examine multiple variables together rather than treating each transaction as an isolated event.
RBIH says MuleHunter is intended to improve detection accuracy while reducing false positives. That second part matters just as much as finding suspicious accounts. An overly aggressive fraud system can inconvenience legitimate customers, freeze normal financial activity and create additional work for bank investigators.
The technical challenge is therefore a balancing act: identify suspicious behaviour quickly without treating unusual but legitimate customers as criminals.
MuleHunter is being connected to a wider fraud-intelligence network
The initiative is also becoming part of a broader government effort rather than remaining a standalone bank tool.
In May 2026, the Indian Cyber Crime Coordination Centre (I4C) under the Ministry of Home Affairs and RBIH signed a memorandum of understanding to strengthen cooperation on cyber-enabled financial fraud. According to the Press Information Bureau, I4C will share mule-account intelligence and suspect identifiers from its Suspect Registry so that RBIH can use the information to improve AI-driven fraud-risk models, including MuleHunter.ai.
This creates an important feedback loop. Information gathered through cybercrime reporting and investigation can help financial institutions identify suspicious patterns, while signals detected by banks can potentially contribute to a broader understanding of fraud networks.
That is especially relevant because cyber fraud rarely respects the boundaries between individual banks. A fraudster can move money across multiple institutions, payment channels and accounts. Sharing intelligence can therefore be more useful than asking every bank to solve the same problem independently.
The system is designed to stay inside the bank
One of the more significant details in RBIH’s technical documentation concerns privacy and deployment.
RBIH says the MuleHunter model runs inside the bank’s own infrastructure and that personally identifiable information is not exchanged between the bank and RBIH. Instead, a standardised and anonymised extract of management information is periodically shared for monitoring model drift, false-positive trends and coverage consistency.
That architecture matters because fraud detection requires access to highly sensitive financial information. A central system that continuously receives identifiable transaction data would create a very different privacy and security risk. Keeping the model inside a bank’s environment can reduce the amount of sensitive customer information that needs to leave the institution.
It does not eliminate risk, however. Banks still have to secure their own fraud-detection infrastructure, govern access to model outputs and ensure that automated risk scores are not treated as unquestionable decisions.
From daily batches to near-real-time detection
Speed is another major part of the project.
RBIH’s documentation describes three deployment stages: T+1 processing, hourly processing and eventually near-real-time detection. The immediate target described by RBIH is the hourly stage, with the longer-term objective of detecting mule activity much closer to the moment suspicious behaviour occurs.
That progression reflects the economics of digital fraud. If a suspicious account is identified a day after the money has moved, the bank may already be trying to recover funds that have passed through several additional accounts. Detecting the pattern within an hour, or eventually within minutes, can give banks a much better chance of interrupting the movement of money.
The government’s March 2026 response to Parliament said MuleHunter was already live in 26 banks. Earlier parliamentary material had highlighted pilot deployments in Canara Bank, Punjab National Bank, Bank of Baroda and Bank of India, with recommendations to expand the system to more public and private-sector banks.
India is also tightening the rules around suspected mule accounts
The technology is arriving alongside changes in how banks are expected to deal with suspected fraud accounts.
In September 2026, the RBI proposed a standardised framework for suspected money-mule accounts and transactions linked to cyber-enabled financial fraud. The proposal included temporary debit restrictions in certain circumstances and a process giving customers time to explain disputed transactions, according to Moneycontrol.
That combination is significant. AI can help a bank decide which accounts deserve closer attention, while a standardised regulatory process can determine what the bank does after an account is flagged.
The difficult part will be making those two layers work together without turning a fraud-prevention system into an opaque mechanism for restricting legitimate customers.
A fraud score should be a warning, not a verdict
There is an important distinction between identifying an account as statistically unusual and proving that its owner participated in a crime.
A customer could receive an unexpected payment, suddenly conduct many transactions or have an account behaviour that resembles a fraud pattern for completely legitimate reasons. Machine-learning models can reduce false positives, but they cannot remove uncertainty from financial crime investigations.
For that reason, the quality of the surrounding human review process will matter almost as much as the model itself. Banks need clear escalation procedures, audit trails, explainable signals where possible and mechanisms for legitimate customers to challenge restrictions.
RBIH’s stated objective of minimising false positives is therefore more than a technical performance metric. It is central to whether customers will trust automated fraud controls.
MuleHunter could become one layer of a much larger system
MuleHunter is also best understood as one component in India’s evolving digital-fraud architecture.
The government has been developing multiple mechanisms around cybercrime reporting, suspect registries and financial-fraud response. RBIH is separately developing the Digital Payments Intelligence Platform, described as a centralised intelligence-sharing platform for reporting and accessing fraud intelligence in near real time across the digital payments ecosystem.
If these systems mature together, the direction is clear: fraud prevention is moving from individual transaction checks towards network-level intelligence. Instead of asking only whether a particular payment looks suspicious, banks can increasingly examine relationships between accounts, transaction histories and previously identified fraud indicators.
That shift could make it harder for fraud networks to exploit the gaps between banks and payment systems. It also raises the importance of governance because a system with access to large-scale financial intelligence needs strong controls over how information is shared, retained and acted upon.
The bigger test is what happens when the system gets it wrong
MuleHunter shows how India is applying AI to a problem where speed, scale and adaptability matter. The technology is designed to help banks identify suspicious accounts earlier, connect fraud intelligence across institutions and move closer to real-time intervention.
But successful fraud detection will not be measured only by the number of accounts a model flags. It will also depend on how many legitimate customers are protected from unnecessary restrictions, how quickly investigators can act on alerts, and whether banks can explain and review automated decisions.
For customers, the most important change may happen quietly in the background. A payment that once triggered an investigation only after money had disappeared could increasingly be evaluated against a much larger set of signals while the transaction chain is still unfolding.
That is the promise of MuleHunter.ai. It is not a single AI system that can eliminate digital fraud. It is an attempt to make the banking system faster and more adaptive at spotting the accounts that allow fraud money to move.
