Media Nama: National: Wednesday, 31 December 2025.
As of December 10, 2025,
23 banks have implemented the Reserve Bank of India’s (RBI) MuleHunter.AI
initiative, according to an RTI response that MediaNama received from the
central bank.
Furthermore, India’s central bank said that it cannot provide data on the number of mule accounts identified or acted upon through MuleHunter.AI because the RBI holds the information in a fiduciary capacity with banks, and that disclosure could harm competitive interests under the RTI Act, 2005.
Moreover, the RBI stated that it does not have information on formal coordination with law enforcement agencies like the Indian Cyber Crime Coordination Centre (I4C) specifically for MuleHunter.AI, nor does it hold details of internal circulars, guidelines, or advisories sent to banks or payment aggregators about the initiative.
For context, MuleHunter.AI is an artificial intelligence-driven (AI-driven) model developed by the Reserve Bank Innovation Hub (RBIH) to help banks detect and curb mule accounts, which fraudsters use to funnel, launder, or transfer proceeds of financial fraud.
Earlier in August 2025, the RBI’s Chief General Manager (CGM) Suvendu Pati said that at least 15 banks had already implemented MuleHunter.AI. These banks include the likes of Canara Bank, Punjab National Bank, Bank of India, Bank of Baroda, and AU Small Finance Bank. At the same event, Pati added that Federal Bank was in advanced stages of going live.
Meanwhile, RBI Governor Sanjay Malhotra noted in November 2025 that nearly 20 banks had adopted the system, again without a published list of names.
What is Mule Hunter?
The RBIH announced MuleHunter.AI in December 2024 as a new AI-based tool focused on identifying mule accounts used in financial fraud. Fraudsters exploit mule accounts to transfer illicit funds through otherwise legitimate banking systems, which makes transactions difficult to trace and recover, and leaves financial institutions as well as customers vulnerable to losses.
Traditional fraud detection systems rely on static, rule-based models that flag accounts based on fixed criteria. However, these systems often generate high false positives and require lengthy manual checks, slowing responses and leaving many fraudulent accounts undetected for longer periods. Moreover, rigid rule sets cannot adapt swiftly to evolving criminal behaviour.
However, MuleHunter.AI analyses transaction and account activity patterns across banks to identify accounts likely used as “mules” in financial fraud, rather than relying on fixed, static rules. It was developed by studying 19 distinct behaviours associated with mule accounts, enabling more accurate and faster detection of suspicious accounts.
AI In Fintech and Banking
AI is increasingly shaping the Indian financial sector, with fraud detection emerging as a primary application alongside customer service and risk management. At MediaNama’s “AI in Fintech” panel in April 2025, industry experts highlighted that banks and fintech firms are experimenting with AI to detect fraud and unusual patterns in transactional behaviour, much like the RBI’s MuleHunter.AI initiative that flags mule accounts involved in financial crime.
Beyond fraud detection, AI supports fintech operations in several other areas. For example, platforms are using algorithms and analysing behavioural and demographic data in order to personalise investment recommendations and tailor customer interactions. However, panellists at MediaNama’s event noted that AI deployment remains limited in critical decisions such as credit scoring and lending, largely due to concerns over historical bias in datasets.
Furthermore, the RBI’s Deputy Governor M. Rajeshwar Rao has also emphasised the need for responsible AI adoption across the banking sector, warning of risks such as algorithmic bias, opaque models, and systemic vulnerabilities in September 2025.
Additionally, the RBI has articulated a broader governance regime for AI through its Framework for Responsible and Ethical Enablement of Artificial Intelligence (FREE-AI). To explain, the FREE-AI framework sets out foundational principles and practical recommendations that aim to guide how banks, fintechs and regulated entities build, deploy and govern AI systems responsibly.
Furthermore, India’s central bank said that it cannot provide data on the number of mule accounts identified or acted upon through MuleHunter.AI because the RBI holds the information in a fiduciary capacity with banks, and that disclosure could harm competitive interests under the RTI Act, 2005.
Moreover, the RBI stated that it does not have information on formal coordination with law enforcement agencies like the Indian Cyber Crime Coordination Centre (I4C) specifically for MuleHunter.AI, nor does it hold details of internal circulars, guidelines, or advisories sent to banks or payment aggregators about the initiative.
For context, MuleHunter.AI is an artificial intelligence-driven (AI-driven) model developed by the Reserve Bank Innovation Hub (RBIH) to help banks detect and curb mule accounts, which fraudsters use to funnel, launder, or transfer proceeds of financial fraud.
Earlier in August 2025, the RBI’s Chief General Manager (CGM) Suvendu Pati said that at least 15 banks had already implemented MuleHunter.AI. These banks include the likes of Canara Bank, Punjab National Bank, Bank of India, Bank of Baroda, and AU Small Finance Bank. At the same event, Pati added that Federal Bank was in advanced stages of going live.
Meanwhile, RBI Governor Sanjay Malhotra noted in November 2025 that nearly 20 banks had adopted the system, again without a published list of names.
What is Mule Hunter?
The RBIH announced MuleHunter.AI in December 2024 as a new AI-based tool focused on identifying mule accounts used in financial fraud. Fraudsters exploit mule accounts to transfer illicit funds through otherwise legitimate banking systems, which makes transactions difficult to trace and recover, and leaves financial institutions as well as customers vulnerable to losses.
Traditional fraud detection systems rely on static, rule-based models that flag accounts based on fixed criteria. However, these systems often generate high false positives and require lengthy manual checks, slowing responses and leaving many fraudulent accounts undetected for longer periods. Moreover, rigid rule sets cannot adapt swiftly to evolving criminal behaviour.
However, MuleHunter.AI analyses transaction and account activity patterns across banks to identify accounts likely used as “mules” in financial fraud, rather than relying on fixed, static rules. It was developed by studying 19 distinct behaviours associated with mule accounts, enabling more accurate and faster detection of suspicious accounts.
AI In Fintech and Banking
AI is increasingly shaping the Indian financial sector, with fraud detection emerging as a primary application alongside customer service and risk management. At MediaNama’s “AI in Fintech” panel in April 2025, industry experts highlighted that banks and fintech firms are experimenting with AI to detect fraud and unusual patterns in transactional behaviour, much like the RBI’s MuleHunter.AI initiative that flags mule accounts involved in financial crime.
Beyond fraud detection, AI supports fintech operations in several other areas. For example, platforms are using algorithms and analysing behavioural and demographic data in order to personalise investment recommendations and tailor customer interactions. However, panellists at MediaNama’s event noted that AI deployment remains limited in critical decisions such as credit scoring and lending, largely due to concerns over historical bias in datasets.
Furthermore, the RBI’s Deputy Governor M. Rajeshwar Rao has also emphasised the need for responsible AI adoption across the banking sector, warning of risks such as algorithmic bias, opaque models, and systemic vulnerabilities in September 2025.
Additionally, the RBI has articulated a broader governance regime for AI through its Framework for Responsible and Ethical Enablement of Artificial Intelligence (FREE-AI). To explain, the FREE-AI framework sets out foundational principles and practical recommendations that aim to guide how banks, fintechs and regulated entities build, deploy and govern AI systems responsibly.
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