Artificial Intelligence and Indian Banking sector

What is Artificial Intelligence (AI)

According to WIXEncyclopedia “AI is a branch of computer science that develops machine systems capable of demonstrating behaviours linked to human intelligence. AI programs use data collected from different interactions to improve the way they mimic humans in order to perform tasks such as learning, planning, knowledge representation, perception and problem-solving”  

Thus, AI is the science of making a computer or a software think intelligently just like a human brain, towards learning how to solve a problem. In a way it helps personalise interaction between a human being and a machine. AI tools are based on three main cognitive skills of learning, replicating and reasoning. AI makes a machine, generally a computer, capable of thinking and responding like a human being. The tasks involving a set of algorithms become automatic through intelligent programming based on learning through experience.

Artificial Intelligence (AI) has been steadily transforming the global landscape in almost every conceivable way. Sam Altman, the Open AI CEO , while launching its generative AI Chatbot Gpt-5, praised usage of AI in India and said that he considers India to be the second biggest market for AI after US.

With such sweeping transformation of the environment, Banking cannot remain unaffected. In this article, we shall be touching basics of AI (for the uninitiated) and how it is currently impacting the Indian banking sector, both in terms of opportunities and hazards.

AI in Banking  

AI is important both for the vendor ie Banks and the user ie the customer as it eases life for both. For the vendor, it enables efficient management of humongous data and usage of such data for smooth operations, while for the customer it provides an immersive experience away from the drudgery of physical interface.

Banks have been quick to adopt AI tools to efficiently manage ever increasing data through automation, machine learning and natural language processing. This is a big change from banking of 80s and 90s. It has helped Banks to efficiently manage increased work load including risk management and at the same time improve customer experience.

For the purpose of enhanced experience in front office and efficient workflows with foolproof security in back office operations, Banks are using several AI tools and technologies. Few key activity areas where AI is bringing profound change to banking, are as under –

Customer Interface – This is the most visible use of AI technology by Banks. It has smoothened the usual transaction grind which was associated with physically visiting the Bank even for small jobs. AI tools generate round the clock auto response through chatbots and virtual assistants to customer’s queries along with personalising the digital banking experience eg fund transfers, SMS/ Whats App  banking services etc. This has been a real game changer when seen in the light of financial inclusion with 9 out of 10 Indians now having a bank account (the World Bank’s Global Findex 2025 report) as against 35 % in 2011. There are an estimated 1.5 billion digital banking transactions every day.

Fraud Detection and control – Banks are taking help of AI tools to track transactions, analyse the same through machine learning and throw up red flags in case of repetitive anomalies and unusual behavioural patterns. Although its difficult to identify fraudulent behaviours before it happens, AI tools can analyse anomalous patterns and indicate likelihood of frauds. Sophisticated AI tools are also being used extensively in cyber security and tracking money laundering.

Backoffice Operations – A Bank’s back office operations, generally known as core banking, involve a large number of repetitive tasks which can be automated through intelligent predictive AI tools. This can reduce operational costs through manpower redeployment and also increase efficiency. In 2018, after the infamous Nirav Modi fraud case, Punjab National Bank had implemented AI systems to improve its reconciliation and audit framework.

Mobile Banking – Banks have extensively used AI tools to bring banking into palms of customers. Most day to day services can be accessed through Bank’s apps on the mobile or through whats app. This has also helped Banks in notifying customers about any modifications in terms & conditions or policy updates.  

Credit administration – Banks have developed special models to deal with standardized loan applications eg housing loans, vehicle loans etc where the risk is automatically assessed by a statistical model based on the background information provided by the customer. CIBIL score of the customer comes in as additional support.

Regulatory compliance - Banks as institutions, are under microscopic regulatory scrutiny with a large number of compliance actions at regular intervals may be on daily, weekly, fortnightly or monthly basis. AI has helped the Banks to automate a major part of these compliance functions and processes and consequent reporting to regulators. In addition to being on the right side pf the law, it also helps the Banks in avoiding regulatory penalties which sometimes can be very heavy.

In addition to above applications, AI is also being used for expanding business and offering new products to customers on the basis of predictive behavioural analysis.

Challenges in AI implementation

There is also a flip side to extensive adoption of AI in Banking. In a lead report by The IBM Institute for Business Value titled “2024 Global Outlook for Banking and Financial Markets” it is observed that “ Following the astonishing rise of generative AI, artificial intelligence has seized the world’s attention. Executives are either dazzled by bright futures or dismayed by dystopian scenarios, and polarizing boardroom discussions proliferate. Banking executives are brainstorming how to assess and prioritize AI’s economic potential, estimate access costs, and manage the risks that come with quickly scaling AI enterprise-wide”.

(https://www.ibm.com/thought-leadership/institute-business-value/report/2024-banking-financial-markets-outlook)

Major concerns associated with implementation of AI in the Financial and Banking ecosystem relate to Cybersecurity, Data privacy, response accuracy and system implementation costs.

Banks in India and AI

Indian Banks. both in the public and private sector, have been in the forefront of implementing AI tools, to keep pace with larger Banks in other parts of the world.

On 8^th February 2017, State Bank of India, the country’s largest Bank announced establishment of Bank chain, a platform and initiative for banks to implement blockchain software. Majority of the banks in India were members and the platform was designed for systems that shared data between its members. Active projects included shared Know Your Customer (KYC), Anti Money Laundering, syndication of loans / consortium lending, trade finance, asset registry, cross border payments, peer-to-peer payments, and blockchain security controls (https://en.wikipedia.org/wiki/Bankchain)

Hereinbelow, we discuss AI adoption by some major Indian Banks.

State Bank of India (SBI) – With a customer base of 52 crores, the Bank has been in the forefront in integrating AI tools with various processes for smooth risk free operations and enhanced customer satisfaction and using predictive analytics for customer retention. In 2017, The Bank had introduced an online chatbot for answering customer queries and attending to complaints. The Bank also uses an internal chatbot askSBI to assist staff with any queries they may have for solving a banking problem. Going forward the Bank plans to adopt Blockchain technology for enhanced security and transparency.

HDFC Bank - The Bank has deployed an AI- based chatbot “Eva” (Electronic Virtual  Assistance), for addressing Customer queries.  In its annual report for FY 2025, the Bank has disclosed that it has identified over 15 programmes that will use generative artificial intelligence (GenAI). Further, it has transitioned from pilot projects to centralised, “platform-driven GenAI strategy” for improving staff productivity and customer service. The bank has also launched a GenAI Academy to accelerate capability in generative AI.(https://www.livemint.com/industry/banking/ hdfc-bank-raises-bets-on-genai-for-productivity-gains-11752500171694.html)

ICICI Bank – The Bank started off with AI-powered virtual assistant - iPal, which addresses customer queries along with account information and facilitates real time transactions. Like other Banks, ICICI also uses AI to analyse customer behaviour and preferences to pitch new products. In back office, machine learning has been used to track unusual transaction activity suggesting fraudulent actions and Robotic process automation for handling repetitive tasks like account opening process, loan application processing for housing / vehicle loans etc.

Axis Bank – According to Bank’s website, AHA the chatbot caters to an extensive list of FAQs as well as a microphone facility for seamless conversation. Powered by Machine learning capability and Natural language processing, Axis AHA provides convenient on demand digital support for financial and non-financial transactions. Robotic Automation process is being used for back office operations.

RBI Committee on AI in financial sector

RBI had set up a committee on 26^th December 2024 with a goal “To encourage the responsible and ethical adoption of AI in the financial sector”, under the Chairmanship of Dr Pushpak Bhattacharya, Professor from IIT Bombay. The committee’s report released on 13^th August, 2025 sets out a framework to guide use of AI in the financial sector, aiming to harness its potential while safeguarding against associated risks.

The Committee has developed 7 Sutras to serve as the foundational principles for AI adoption. Guided by these sutras, the Committee has recommended a forward-looking approach, containing 26 actionable recommendations under six strategic pillars. The report envisions a financial ecosystem where encouraging innovation is in harmony, and not at odds, with mitigation of risk. (www.RBI.org.in)

The seven Sutras to guide AI adoption in the financial sector. are (i) Trust is the Foundation (ii) People First (iii) Innovation over Restraint (iv) Fairness and Equity (v) Accountability (vi) Understandable by Design (vii) Safety, Resilience and Sustainability.

Using Sutras as guidance, the Committee has recommended an approach through a unified vision spread across 6 strategic Pillars that address the dimensions of innovation and mitigate risks -

• establishment of shared infrastructure to democratise access to data and creation of an AI Innovation Sandbox

• development of indigenous financial sector-specific AI models

• formulation of an AI policy to provide necessary regulatory guidance

• institutional capacity building at all levels,

• sharing of best practices and learnings across the financial sector

• a more tolerant approach to compliance for low-risk AI solutions to facilitate inclusion and other priorities

Modification in the existing AI structure based on RBI committee recommendations will further contribute to enhance customer experience, improve productivity, increase profits by reducing operational costs and ensure compliance at all stages.

Conclusion

AI has heralded the biggest transformation in banking industry after computerisation in 1990s and 2000s. It has totally revolutionised customer experience while at the same time enabled banks to manage data in a secure, risk free and efficient ecosystem. With the burgeoning customer base, post financial inclusion initiative of the Government, Banks are realising that AI is not only important for improving customer experience but also to run the systems efficiently. This is all the more important for making systems future ready. According to a paper titled “Building the AI Bank of the future” published by McKinsey & Co in May 2021 AI is set to unlock over USD One trillion in annual value for the global banking industry by 2030.

Apart from revolutionising customer interface, AI tools have facilitated Bank’s internal workflow. A process like Credit risk assessment which was being done manually earlier with a limited number of variations, is now being automated with endless number of probabilities built into the model. Same is the case with Regulatory compliance and generation of multiple periodical reports. This has also enabled redeployment of manpower and save costs.

Automation sometimes comes with its own pitfalls. With such large databases on one platform, Banks have to guard against data breach and cybersecurity. Another worry for Banks would be availability of skilled manpower for integration of AI tools with Bank’s own systems.

To conclude, as Banks in India move to next phase of AI implementation, the challenge would be to provide a secure risk free ecosystem. In the long run it should be a win-win situation for all the stakeholders – the Banks, the regulators and the customers.