Indian govt, RBI Innovation signs AI pact to tackle financial frauds

AhmadJunaidBlogMay 12, 2026362 Views


The Indian Cyber Crime Coordination Centre (I4C), operating under the Union Ministry of Home Affairs, has signed a Memorandum of Understanding (MoU) with the Reserve Bank Innovation Hub (RBIH) to strengthen defences against cyber-enabled financial frauds and mule accounts across the banking and digital payments ecosystem. 

With the partnership, the government plans to develop an in-house Artificial Intelligence-based solution to identify and curb risks surrounding mule accounts and enhance its fraud detection capabilities. Home Minister, Amit Shah, shared an X post announcing the MoU. The post said, “Modi govt is tirelessly working for cyber-secure Bharat.”

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“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,” he added. It is reported that the latest agreement is expected to integrate data from I4C’s Suspect Registry with RBIH-backed AI-powered fraud detection tools to identify suspicious accounts faster and curb fraudulent transactions before funds are siphoned off.

RBI has been scaling its AI-led efforts to tackle the misuse of mule accounts. Its “MuleHunter.AI” platform, which is an AI and machine learning-based mule account detection system, is being used by over 26 banks.

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Now, banks are also advised to deploy advanced real-time transaction monitoring tools and AI/ML systems to detect suspicious transaction patterns. With the MoU, the government is strengthening these capabilities by combining financial intelligence with cybercrime investigation data.

The government press not further added that “It is working towards strengthening the security and resilience of the digital payments ecosystem and increasing citizen confidence in the digital banking ecosystem through technology-driven initiatives and the development of AI-enabled fraud detection frameworks.”



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