Artificial intelligence is entering a new phase in financial services. After years of banks using generative AI for chatbots, document summarisation and employee assistance, the industry is increasingly exploring agentic AI systems that can take actions, coordinate multiple tasks and complete parts of business workflows with limited human intervention.
The shift is emerging as a major theme at Global FinTech Fest (GFF) 2026, where agentic AI is being examined as a technology capable of changing how financial institutions manage operations, customer service, risk and compliance.
Unlike conventional AI applications that primarily respond to a request, agentic AI is designed to understand an objective, evaluate available information, determine the next step and execute a sequence of permitted actions.
For banks processing millions of transactions and customer interactions, that capability could have significant operational implications.
From AI Assistance to Workflow Execution
Traditional automation generally depends on predefined rules. A system performs a specific task when certain conditions are met.
Agentic AI introduces a more flexible approach. An AI agent can potentially assess a situation, decide which task should happen next and coordinate several connected activities before reaching a defined outcome.
Consider a loan application. The process can involve customer onboarding, document collection, verification, credit assessment, underwriting and compliance checks.
Instead of automating each activity separately, an agentic system could coordinate multiple steps. It could collect required information, check documents against established policies, route cases to the appropriate team and escalate exceptions to human employees.
The same concept can be applied to fraud monitoring. An AI agent could identify an unusual transaction, examine related signals and initiate an approved response while operating within predefined institutional controls.
This makes agentic AI particularly relevant to financial institutions because many banking processes involve multiple connected decisions and repetitive operational steps.
Banks Explore New Agentic AI Use Cases
Indian banks are already examining where agentic workflows could provide practical benefits.
State Bank of India has been exploring agentic applications across areas including risk management, underwriting, personal finance management, customer onboarding and internal reporting. The bank has indicated that agentic workflows could supplement or eventually take over portions of different processes, provided appropriate guardrails are in place.
HDFC Bank is also exploring AI agents for customer service, back-office operations and personalised services.
These developments indicate that financial institutions are moving beyond experimentation with AI assistants and beginning to evaluate AI systems based on their ability to execute business processes.
However, this transition is unlikely to happen uniformly across banking operations.
Why Financial Services Are Well Suited to Agentic AI
The financial sector generates enormous volumes of structured and unstructured data every day. Banks process transactions, customer documents, regulatory information, applications and service requests continuously.
Many workflows also operate according to established policies and procedures. This creates opportunities for AI systems to coordinate repetitive activities while allowing employees to focus on complex cases.
Potential applications include:
- Fraud detection – Identifying suspicious activity and initiating approved responses.
- Customer onboarding – Coordinating document collection, verification and account-opening processes.
- Underwriting – Gathering information and supporting credit assessment workflows.
- Compliance – Organising documentation and flagging potential exceptions.
- Customer service – Handling multi-step service requests rather than simply answering questions.
- Personal finance – Supporting customers with budgeting, financial insights and personalised recommendations.
- Back-office operations – Automating repetitive internal processes and document verification.
The biggest opportunity may therefore not be replacing employees but reducing the amount of manual coordination required between people, systems and departments.
Financial Institutions Are Likely to Start With Lower-Risk Processes
Despite the potential, banks cannot deploy autonomous AI in the same way technology companies might deploy consumer-facing software.
Financial institutions operate in highly regulated environments where errors can have financial, legal and reputational consequences. As a result, early adoption is expected to concentrate on lower-risk internal processes.
Potential starting points include internal document verification, employee workflows and administrative processes where decisions can be reviewed before they affect customers.
This gradual approach could allow banks to test agentic systems, measure their performance and establish appropriate controls before expanding them into more sensitive areas.
Regulation Will Shape the Pace of Adoption
Governance will be one of the biggest factors determining how quickly agentic AI moves into mainstream financial services.
The Reserve Bank of India has already taken steps toward developing a framework for responsible and ethical AI adoption in the financial sector. The central bank established a committee to work on the Framework for Responsible and Ethical Enablement of AI (FREE-AI).
For agentic systems, governance becomes particularly important because these technologies are designed to take actions rather than simply generate information.
Financial institutions will need to establish clear boundaries around what an AI agent can access, which decisions it can make, which actions it can execute and when a human employee must intervene.
Auditability, data protection, explainability, security and accountability will therefore become critical components of enterprise agentic AI deployments.
Conclusion
The rise of agentic AI represents a broader change in the financial industry’s approach to artificial intelligence.
The first phase focused largely on prediction and automation. The generative AI wave introduced systems capable of producing content, answering questions and assisting employees. Agentic AI could represent the next step: systems that combine reasoning, decision-making and execution within defined boundaries.
For banks, the value proposition will ultimately depend less on whether an AI agent can perform an impressive demonstration and more on whether it can deliver measurable improvements in efficiency, accuracy, customer experience and risk management.
GFF 2026’s focus on agentic AI highlights that the financial sector is beginning to look beyond AI as an assistant and toward AI as an operational participant.
The transition will likely be gradual, heavily governed and focused initially on controlled workflows. But if financial institutions can establish the right safeguards, agentic AI could become an important layer in the future architecture of banking and financial services.
