Agentic AI, the next phase in AI evolution, is set to transform the financial services industry with autonomous, conversational agents capable of intelligent decision-making and workflow automation. Leading banks like HDFC, SBI, and Wells Fargo are exploring its potential across operations—from customer service and risk management to personal finance and onboarding.
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Artificial intelligence (AI) has evolved from simple data-driven models to autonomous systems capable of reasoning and action. After predictive AI, which forecasts outcomes using historical data, and generative AI, which creates new content like text and images, the latest leap is agentic AI—a powerful form of AI that goes beyond content generation. Agentic AI introduces autonomous, goal-driven agents that can interact with systems, perceive their environments, take actions without human intervention, and learn from feedback to adapt.
These AI agents are designed for conversational interaction and task execution, enabling automation that is not just intelligent, but context-aware and self-improving. Seen as one of the top strategic tech trends, agentic AI is expected to transform sectors like finance, healthcare, and manufacturing by acting as digital labor—automating decisions, adapting to changing scenarios, and saving time and resources.
In the financial services sector, giants like HDFC Bank, State Bank of India (SBI), and Wells Fargo are already exploring how to embed this technology across key functions. These firms, which move billions of dollars every hour across asset classes and geographies, see agentic AI as a tool to automate workflows and enhance operational efficiency.
Indian banks explore automation potential
India’s largest private bank by market capitalization, HDFC Bank, plans to deploy agentic AI for customer service automation and streamlining back-office operations. “We are looking at agents to expose domain level tasks to our AI-driven use cases,” said Ramesh Lakshminarayanan, CIO & Group Head-IT, HDFC Bank. “So we are planning to automate a lot of routine tasks across the bank – like customer service automation, back-office operations, and offering personalised services – through AI agents.”
SBI is also evaluating similar applications across multiple domains. Nitin Chugh, Deputy Managing Director and Head (Digital Banking & Transformation), said the bank is “studying and planning to experiment” with workflows where agentic AI could be deployed. A decision will be taken in the next two months on whether to build such systems in-house or opt for partnerships. “Any process you pick up, I think there is room for an agentic workflow to at least supplement it, if not take over it completely, within a set of guardrails given the risks,” Chugh said. Possible use cases include personal finance management, risk and underwriting workflows, intelligent report creation, software development, onboarding lifecycle management, and testing or training workflows.
Wells Fargo bets on hyper-personalisation
Wells Fargo, a leading U.S. commercial lender, sees agentic AI as key to enhancing customer experience and personalization. “You really have to start thinking differently. If you have these digital agents and they can provide that level of information, how do you want to use that? How do you want to look at transforming your business?” said Tracy Kerrins, Head of Enterprise Generative AI and CIO at Wells Fargo. She believes agentic AI enables businesses to “think differently, work differently” and reshape how operations and workforce structures are designed.
Unlike traditional GenAI bots, agentic AI involves multiple AI agents working in coordination, often interfacing directly with enterprise systems. This allows businesses to automate complex workflows in a contextual and intelligent manner—well beyond the capabilities of earlier rule-based automation tools. These agents operate within governance frameworks and can perform tasks autonomously, reducing the need for human intervention in routine operations.
Shift towards structured implementations
Lakshminarayanan noted that in the financial sector, there’s a clear shift from generic large language models to “structured, controlled, and specialised implementations.” The goal is to achieve “seamless automation while maintaining built-in controls and compliance.”
In December, the Reserve Bank of India formed a committee to develop a Framework for Responsible and Ethical AI (FREE-AI), which is expected to provide regulatory guidance for financial institutions adopting such technologies.
According to Rohit Pandharkar, Partner at EY India, agentic AI is likely to gain a foothold in the Indian BFSI sector over the next 12 months, starting with internal processes. For instance, in loan onboarding, AI agents could coordinate tasks across legal, compliance, and underwriting teams, applying company SOPs and reasoning through tasks automatically. While human oversight will remain crucial, such tools may begin to demonstrate real ROI in time and effort saved.
Ashok Krish, Global Head – AI at Tata Consultancy Services, pointed out that in areas like insurance, much of the work—such as reviewing emails, forms, and eligibility assessments—still relies heavily on humans. Agentic AI, he said, can ease this burden. The emergence of small language models has further accelerated the development of domain-specific AI agents.
Firms like TCS and Infosys are already building AI agents tailored for different industries. Infosys, for instance, said agentic AI can support periodic risk reviews, compliance reporting, and regulatory adaptation. HSBC has already used this technology to automate credit risk management with real-time capital allocation, setting an example for others in the sector.
