Reserve Bank of India Governor Sanjay Malhotra has highlighted the growing importance of artificial intelligence in the fight against financial fraud, saying AI based systems could become an important tool for banks dealing with increasingly sophisticated fraud attempts.
Speaking at the FIBAC 2026 conference on Tuesday, Malhotra said artificial intelligence would be central to tackling frauds that are themselves increasingly enabled by technology. He argued that traditional rule based systems may find it difficult to respond quickly enough when fraudsters continuously change their methods.
The RBI Governor’s comments come at a time when digital banking and electronic payments have expanded rapidly in India. As more financial transactions move online, banks and financial institutions are also facing new forms of cyber enabled fraud.
Traditional fraud detection systems generally depend on predefined rules. For example, a bank may establish thresholds or patterns that trigger an alert when a transaction appears unusual. Such systems can be effective for known types of suspicious activity, but rapidly changing fraud techniques can make it difficult to maintain static rules.
Artificial intelligence and machine learning can provide a different approach. These technologies can analyse large volumes of transactions and identify patterns that may not be immediately visible through conventional systems.
Machine learning models can be trained to identify unusual behaviour by examining transaction histories, account activity and relationships between different accounts. When properly designed and monitored, such systems can help banks identify suspicious activity more quickly.
The RBI has already taken steps to encourage the use of artificial intelligence and machine learning in fraud prevention. One important initiative is MuleHunter.AI, a system designed to help identify mule accounts that can be used to move or conceal money obtained through fraudulent activities.
According to information provided by the government in March 2026, MuleHunter.AI was already live in 26 banks and was being expanded further. Banks have also been advised to use real time transaction monitoring, AI and machine learning tools and network analytics to identify suspicious transaction patterns and mule account networks.
The central bank’s approach reflects the changing nature of financial crime. Fraudsters can use technology to automate attacks, create convincing communications and modify their methods quickly. This creates a need for financial institutions to improve their ability to identify unusual behaviour as it develops.
However, the increasing use of AI in banking also creates new risks. AI systems themselves can make errors, produce biased outcomes or become vulnerable to manipulation. For this reason, regulators have stressed the importance of strong governance and human oversight.
The RBI has proposed a framework for managing risks associated with artificial intelligence and other models used by banks and financial institutions. The draft framework includes provisions for human oversight and the ability to suspend or deactivate an AI model if it produces unacceptable results.
This approach indicates that AI is not being viewed as a replacement for human responsibility. Instead, financial institutions are expected to maintain controls around the technology and ensure that people remain accountable for important decisions.
The issue is particularly significant because banks increasingly use technology for several functions. AI can be applied to fraud monitoring, customer service, credit assessment, risk management and other banking operations.
RBI officials have also highlighted the importance of ensuring that AI systems used in finance remain explainable, fair and accountable. Deputy Governor Swaminathan J has said that AI can improve efficiency and financial inclusion but warned about risks including bias, lack of transparency, data misuse and cyber threats.
The growing use of AI in fraud prevention also requires access to reliable information. If banks operate independently without sufficient information about suspicious accounts, identifying fraud networks can become more difficult.
In May 2026, the Indian Cyber Crime Coordination Centre and Reserve Bank Innovation Hub signed an agreement to strengthen information sharing related to mule accounts and cyber financial frauds. Under the arrangement, information from the I4C Suspect Registry can be used to strengthen AI driven fraud detection systems such as MuleHunter.AI.
Such information sharing could help banks identify suspicious accounts more effectively by combining transaction information with broader intelligence about suspected financial crime.
The RBI Governor’s comments also highlight the changing nature of the technology arms race between financial institutions and fraudsters. As criminals adopt more advanced digital tools, banks may need equally sophisticated systems to detect abnormal activity.
Real time monitoring is expected to remain particularly important. Financial fraud can sometimes involve multiple transactions conducted rapidly across different accounts. Detecting these patterns early can potentially help institutions intervene before losses increase.
Network analysis can also help identify relationships between accounts. A single suspicious transaction may not appear unusual in isolation, but a series of transactions involving several linked accounts could reveal a broader fraud network.
At the same time, AI based systems must be carefully monitored to prevent legitimate transactions from being incorrectly blocked. Excessive false alerts can create inconvenience for customers and increase the workload for bank employees.
Data privacy is another consideration. AI fraud detection systems require large amounts of information to identify patterns. Banks therefore need appropriate safeguards to protect customer data and ensure that information is used for legitimate purposes.
Cybersecurity is also becoming increasingly important as financial institutions become more dependent on AI systems. The RBI’s Financial Stability Report published in June 2026 identified AI enabled cyberattacks as a major risk for banks and non banking financial companies.
The development means banks face challenges on two fronts. They must use AI to detect fraud while also protecting their own systems from attacks that may use AI.
For customers, stronger AI based fraud detection could eventually result in faster identification of suspicious transactions and improved protection against certain forms of digital financial crime. However, technology alone cannot eliminate fraud.
Customer awareness, secure banking practices, strong authentication, effective internal controls and timely reporting of suspicious activity will continue to remain important.
The RBI’s latest comments therefore point towards a broader transformation in the way financial fraud is monitored. Artificial intelligence is increasingly becoming part of the banking sector’s defensive infrastructure, particularly as fraud techniques become more sophisticated.
The central bank’s focus is not simply on adopting new technology but also on ensuring that AI systems are governed responsibly. Human oversight, data protection, transparency and the ability to intervene when an AI model fails are expected to remain important safeguards.
For Indian banks, the challenge will be to balance technological innovation with reliability and accountability. AI can process large amounts of information quickly and identify patterns that traditional systems may miss, but its effectiveness depends on the quality of data, model design, monitoring and governance.
The RBI Governor’s remarks underline the growing role of AI in the fight against technology driven financial crime. As digital banking continues to expand, banks are likely to invest further in machine learning, real time monitoring and network based fraud detection.
The next phase of India's banking security framework is therefore expected to involve greater use of AI alongside human oversight and regulatory safeguards. The objective will be to make digital financial services safer while maintaining customer trust and protecting the integrity of the banking system.