Indian Banks Resort To GenAI For Efficiency, Productivity: EY Report

Indian banks are also growing more and more adoptive towards using Generative AI (GenAI) in pursuing productivity, automation, and better customer experience. According to the latest EY India report, The AIdea of India 2025, GenAI is capable of augmenting banking processes' productivity by 44 to 46 per cent primarily across sales, customer service, and credit/collections.

The report informs us that the sales and customer service operations can contribute to a possible gain in productivity of 38 to 40 per cent, and collections and credit would increase by 34 to 36 per cent. All three put together have the ability to add to 42 per cent of the total industry gain in productivity.

Banks are using GenAI to streamline mundane work, enhance data analysis, and improve decision-making. It is poised to reduce manual labour, improve accuracy, and speed up customer-facing roles as well as back-end operations.

How GenAI Is Boosting Banking Services

Banks are introducing GenAI in ways to improve customer interaction and internal workings. GenAI-based virtual assistants and chatbots are becoming increasingly popular in customer service. They perform routine inquiries like account balance, transactional updates, and credit card information. Automated handling of these activities is helping banks to respond in a quicker way and freeing up human agents of workload.

GenAI is also used to provide personalised financial guidance. Intelligent banking software examines customers' expenditure records to identify spending habits, predict future spending, and suggest savings or investment schemes. This allows customers to make intelligent financial decisions while improving engagement with banking facilities.

Others are going one step further with the application of Agentic AI, a higher-order form of AI with capabilities to plan, reason, and perform difficult activities autonomously. Banks are deploying these with core banking systems to automate tasks such as onboarding customers, checking documents, and risk management.

In lending, GenAI technology is optimising the credit assessment process by analysing the customers' data, transaction history, and money flow patterns. This will, through automation, help banks avoid paperwork, accuracy, and a rise in the loan sanction ratio.

Challenges In GenAI Adoption

Indian banking uptake of GenAI is, however, faced with some key challenges. Data security and data privacy are significant roadblocks. Indian data localisation regulations require keeping sensitive information in India, which means certain foreign GenAI models are untenable because they data-cache on distant cloud servers.

Banks are opting to keep GenAI endpoints local in India so that data stays local-regulation-compliant. Others are taking out sensitive personal information from data before feeding it into AI models to minimise privacy issues.

Banks are also using Virtual Private Cloud (VPC) environments to securely run GenAI systems. This approach reduces the risk of exposing customer data while still enabling banks to harness GenAI’s capabilities.

Another of the challenges is the lack of trained talent required in order to develop and maintain GenAI systems. While the larger banks have begun investing in AI-specific staff, smaller banks cannot afford the skills they need in order to adopt mass usage.

Infrastructure costs are also a concern. While GenAI solutions are becoming cheaper, initial installation fees — particularly for secure data environments and customised models—are still too steep. To offset this, there are banks that are embracing hybrid approaches that strike a balance between on-premise and cloud-based APIs in controlling costs.

In addition to that, banks are also putting efforts to deal with model precision problems. Since GenAI models are developed using humongous data sets, errors in training data may generate skewed or incorrect results. In most of these institutions, human intervention is being introduced into decision-making and performing routine audits to reduce the chances of such risks.

Current Adoption Trends in Financial Institutions

The EY report indicates that while GenAI adoption is on the rise, very few institutions have progressed beyond the experimentation stage. 36 per cent of Indian firms surveyed have allocated GenAI investment budgets, and another 24 per cent are piloting applications across departments. Just 15 per cent, however, have deployed GenAI solutions into production environments.

Among the banking and financial industries, NBFCs have been quicker in adopting GenAI. But the big banks have now also started investing aggressively in customer care, cyber security, and lending.

In cyber security, GenAI tools are being programmed to detect suspicious behaviour by looking at patterns of transactions and marking anomalies. This may help banks identify fraud risks at the earliest and respond to them in real-time.

In credit, GenAI supports credit score models. Through analysis of the manner in which individuals spend, earn, and have spent previously, GenAI solutions are helping banks with more informed lending and quicker approvals.

As GenAI adoption is growing among Indian banks, these kinds of technologies will transform the customer experience, along with business operations. 

However, banks must cross the thresholds of protecting data confidentiality and developing infrastructure readiness if they are to exploit the potential of GenAI to the hilt. Investments in training AI, security systems, and localised models will be key to generating large-scale take-up.