The Reserve Bank of India, working alongside state level authorities such as the Haryana government’s State Level Coordination Committee, is pushing a coordinated response to evolving financial fraud, combining artificial intelligence based detection, upgraded public complaint infrastructure, and expanded financial literacy outreach. At the centre of this push is MuleHunter.ai, an AI and machine learning system developed by the Reserve Bank Innovation Hub, now deployed across 31 major banks specifically to identify mule accounts used to move the proceeds of fraud.
For risk and fraud management professionals in banking, this development is a clear signal of where fraud detection capability is headed, systematic, AI driven pattern detection operating at scale across the banking system, not just isolated, institution specific monitoring.
What MuleHunter.ai Actually Does
MuleHunter.ai is designed to help banks detect suspicious account activity that conventional monitoring often struggles to catch.
- The system uses artificial intelligence and machine learning to identify unusual transaction patterns across accounts
- It is specifically aimed at detecting mule accounts, accounts used by cybercriminals to receive and rapidly move proceeds of fraud across multiple channels
- Deployment across 31 major banks means the system can potentially observe patterns that span institutions, not just transactions within a single bank’s own customer base
- The tool was developed by the Reserve Bank Innovation Hub, positioning it as a centrally driven capability rather than something each bank had to build independently
Read Now: RBI Fraud Risk Management Directions 2026: 7 Timelines Banks Must Track
Why Mule Accounts Remain a Major Cybercrime Concern
Mule accounts sit at the centre of how modern financial cybercrime actually operates.
- Criminal networks rely on multiple bank accounts and digital payment channels to rapidly move money obtained through fraud
- Moving funds through several accounts in quick succession complicates efforts to trace stolen money back to its ultimate beneficiaries
- Conventional, rules based transaction monitoring often struggles to catch these patterns, since individual transactions can look unremarkable in isolation
- Automated, AI based detection systems are better suited to spotting unusual patterns across large volumes of accounts and transactions than manual or purely rules based review
The SACHET Portal Upgrade
Alongside MuleHunter.ai, RBI and state authorities reviewed the revamped SACHET portal, used for complaints and market intelligence related to unauthorised financial activities.
- The upgraded portal launched on June 25, 2026, with an improved user interface and workflow
- It is now available in 13 Indian languages, widening accessibility significantly
- Accessibility features for persons with disabilities have been improved
- A tutorial video has been added to help users understand the complaint filing process
- Officials were directed to increase public awareness so more people report suspicious financial activity through the portal
Emerging Fraud Vector: Online Gaming and Family Payment Accounts
The committee also flagged a growing fraud pattern connecting children’s online gaming activity to family payment accounts.
- Fraudsters lure young users with fake in-game rewards, prizes, or other benefits
- Once engaged, criminals attempt to exploit payment methods or bank accounts linked to the user’s parents
- The fraud often does not involve a direct request for money, instead using suspicious links, payment requests, and manipulation to get transactions authorised
- This reflects a broader pattern where family shared financial access, through UPI, saved cards, or linked wallets, creates fraud exposure that traditional adult focused fraud awareness does not address
Read Now: RBI Releases Draft Guidance on Model Risk Management
Broader Financial Awareness Efforts
RBI’s response extends beyond technology deployment into public education.
- More than 25,000 financial literacy programmes have been conducted across Haryana alone
- These initiatives aim to improve public understanding of safe banking practices and fraud protection
- RBI has launched a podcast series titled RBI Talks: Paisa to Policy on YouTube, aimed at making financial knowledge more accessible
- Officials emphasised that combating financial fraud requires technology, public awareness, and coordinated action between institutions working together, not any single measure in isolation
What This Means for Bank Risk and Fraud Teams
The MuleHunter.ai deployment reflects a broader shift risk professionals need to prepare for.
- AI based fraud detection is moving from pilot stage to system wide deployment across major banks
- Fraud teams need to understand how AI flagged accounts are investigated, escalated, and reported, since a system generated flag still requires human judgement before action
- Coordination between banks, regulators, and law enforcement is becoming more central to fraud response, given how mule account networks span multiple institutions
- Emerging fraud vectors, like gaming linked family account fraud, require awareness and detection capability that goes beyond traditional adult focused fraud typologies
Conclusion
RBI’s deployment of MuleHunter.ai across 31 banks, alongside the SACHET portal upgrade and expanded financial literacy outreach, signals a coordinated, technology led approach to fraud detection that banking risk teams need to stay closely aligned with. Institutions that build capability around AI assisted fraud detection now will be better positioned as this kind of system wide monitoring becomes the norm rather than the exception.
Build This Capability with RMAI
The Online Certificate Course in Fraud Risk Management covers fraud detection techniques, red flags, and investigation practices directly relevant to this development. The Online Certificate Course in Risk Management for Artificial Intelligence helps professionals understand how AI based detection systems like MuleHunter.ai fit within a broader risk and governance framework.
