Artificial intelligence has already changed the financial industry.
Banks use AI to detect fraud. Fintech companies use machine learning to assess risk. Payment companies use algorithms to identify suspicious transactions. Wealth platforms use AI to personalise financial recommendations.
But the next stage of this transformation is fundamentally different.
Instead of AI simply analysing information or generating an answer, financial institutions are beginning to explore AI systems that can understand a situation, make decisions within defined boundaries, and take action.
This is the idea behind Agentic AI.
At Global Fintech Fest 2026 in Mumbai, Agentic AI was at the centre of the conversation about the future of global financial services.
GFF 2026 was held from 8–11 September 2026 at Jio World Centre and Trident BKC, Mumbai, under the theme “Potential to Impact: Trusted, Connected, Global Systems for Inclusive Finance.” Agentic AI, tokenisation, and quantum technologies formed the event's three core technology pillars.
What Is Agentic AI?
Agentic AI refers to AI systems that can go beyond providing information and perform tasks or coordinate workflows based on goals, rules, and available data.
A traditional AI system might answer:
“A customer's credit score has decreased.”
An agentic system could potentially take the next steps:
Detect → Analyse → Decide → Act → Monitor → Escalate
For example, an AI agent operating within a bank could identify a change in a customer's financial behaviour, assess the relevant information, determine what action the institution's policies permit, initiate an appropriate workflow, and escalate the case to a human when required.
This creates a significant shift.
Traditional AI
Human → AI → Recommendation → Human action
Agentic AI
Human → AI agent → Decision/workflow → Action → Continuous monitoring
The important word is governance.
In financial services, autonomy cannot mean unrestricted decision-making.
Agentic AI must operate within defined permissions, compliance requirements, security controls, and human oversight.
Why Agentic AI Matters to Financial Services
Financial services are particularly suitable for agentic AI because they involve enormous amounts of:
Structured data
Customer interactions
Transactions
Rules
Regulatory requirements
Risk assessments
Repetitive workflows
Real-time decisions
Banks and fintech companies already operate thousands of processes every day.
The opportunity is to make some of these processes increasingly intelligent and automated.
According to the official GFF 2026 framework, Agentic AI can support autonomous orchestration of complex financial workflows, personalised services at scale, and continuous risk monitoring with reduced human intervention.
This could transform how financial institutions operate.
Agentic AI in Banking
Banking could become one of the biggest beneficiaries of agentic AI.
Imagine a customer asking an AI financial assistant:
“I want to reduce my monthly expenses and start investing ₹10,000 every month.”
A conventional chatbot might provide general suggestions.
An agentic financial assistant could potentially:
Analyse permitted account information.
Identify recurring expenses.
Categorise spending.
Identify potential savings.
Present investment options.
Explain associated risks.
Ask for consent before taking financial action.
Execute approved instructions.
Monitor the customer's financial goals.
The technology therefore changes the role of AI from answering questions to orchestrating financial journeys.
However, such systems would require strict consent, security, auditability, and regulatory controls.
Agentic AI and Customer Experience
One of the most visible applications of Agentic AI could be customer engagement.
Financial customers interact with banks through:
Mobile applications
Websites
Chat
Voice
Email
Branches
Payment platforms
Agentic AI could connect these interactions into a more continuous customer journey.
Instead of treating every customer interaction as a separate conversation, an AI system could potentially understand context and provide more personalised assistance.
This was reflected in a GFF 2026 roundtable focused on “Trusted Conversations in the Age of Agentic AI”, where participants discussed AI-powered customer engagement across banking, payments and digital channels, alongside identity, fraud prevention, privacy and responsible governance.
The challenge is clear:
Financial institutions need AI that is not only intelligent, but trustworthy.
Agentic AI for Fraud Detection
Fraud is another area where agentic AI could have significant potential.
Traditional fraud systems typically identify suspicious patterns and generate alerts.
The next generation could potentially connect multiple steps of the process.
For example:
Transaction anomaly detected
↓
Customer behaviour analysed
↓
Risk assessed
↓
Additional verification triggered
↓
Transaction temporarily restricted if authorised
↓
Case escalated if necessary
↓
Risk continuously monitored
The objective is not necessarily to remove humans from the process.
Instead, AI agents could help financial institutions respond faster while allowing human teams to focus on complex or high-risk decisions.
GFF's cybersecurity and trust discussions specifically explored agentic AI for detecting and responding to threats, alongside fraud, outage risk, third-party exposure, re and AI governance.
Agentic AI and Compliance
Financial institutions operate under extensive regulatory requirements.
Compliance teams must monitor:
Regulations
Circulars
Customer documentation
Transactions
AML requirements
Risk indicators
Internal policies
Regulatory reporting
This creates another potential application for agentic AI.
The SEBI Securities Market TechSprint at GFF 2026 included an “Agentic Compliance” challenge focused on transforming regulatory text into structured, machine-actionable, and auditable compliance logic for financial-market participants.
This is an important development.
Instead of AI simply summarising a regulation, an agentic system could potentially help translate regulatory requirements into operational workflows.
That could mean:
Regulation → Interpretation → Compliance rule → Workflow → Monitoring → Audit trail
Such systems could significantly reduce the time required to identify and operationalise regulatory changes.
But because regulatory decisions are high-stakes, human oversight and auditability remain essential.
Agentic AI and Digital Lending
Digital lending is another area where agentic AI could change the customer journey.
A traditional digital lending process might involve:
Application → Documentation → Verification → Credit assessment → Approval → Disbursement
Agentic AI could potentially coordinate different parts of this workflow.
For example:
Gather permitted information
Verify documentation
Identify missing information
Analyse financial data
Apply approved underwriting rules
Detect unusual behaviour
Recommend an outcome
Trigger the next workflow
Monitor post-disbursement risk
The objective would not simply be faster lending.
It would be more connected lending.
GFF 2026's Payments & Lending track specifically highlighted intelligent real-time credit, new underwriting models,s and inclusion through digital rails.
Agentic AI and Financial Inclusion
Perhaps the most interesting opportunity is the potential impact on financial inclusion.
AI-powered financial systems could potentially help financial institutions serve customers who have historically been difficult or expensive to reach.
This could include:
Small businesses
Rural customers
Farmers
Micro-enterprises
First-time borrowers
Underserved communities
At GFF 2026, rural economic empowerment was one of the dedicated thematic tracks, with a focus on digital finance for agriculture, MSMEs, and cooperatives.
NABARD's GFF 2026 hackathon also focused on using data-driven intelligence to strengthen rural financial inclusion and improve understanding of the financial health of rural enterprises.
This demonstrates an important point:
Agentic AI is not only about making financial institutions more efficient. It could also help extend financial services to more people.
SBI and Agentic AI at GFF 2026
The practical interest in Agentic AI was also visible through the State Bank of India Hackathon at GFF 2026.
SBI challenged fintechs and startups to develop Agentic AI solutions addressing:
Customer acquisition
Digital adoption
Personalised customer engagement
Intelligent financial interactions
AI-led banking journeys
The initiative demonstrates how financial institutions are beginning to explore concrete applications rather than treating Agentic AI only as a theoretical technology trend.
Agentic AI Is Not Just Another Chatbot
This distinction is important.
A chatbot generally responds to a user.
An AI agent can potentially:
Observe → Reason → Plan → Execute → Learn/Adapt within controls
That means the future financial assistant could become more like an intelligent operating layer connecting different systems.
For example:
Customer asks:
“I want to buy health insurance.”
Agentic financial system:
Understands requirement
↓
Reviews permitted customer information
↓
Identifies suitable options
↓
Explains differences
↓
Requests customer approval
↓
Initiates application
↓
Coordinates verification
↓
Tracks application
↓
Updates customer
The customer experiences one continuous journey rather than interacting separately with multiple departments and systems.
The Biggest Challenge: Trust
The biggest question surrounding Agentic AI in finance is not:
“Can AI perform the task?”
It is:
“Can AI be trusted to perform the task?”
Financial services deal with people's money, identities, investments, credit, and personal information.
A poorly governed autonomous system could create significant consequences.
Therefore, financial institutions will need strong controls around:
Identity
Who is interacting with the AI?
Consent
What has the customer authorised?
Permissions
What is the AI allowed to do?
Explainability
Why did the system make a particular recommendation or decision?
Auditability
Can the institution reconstruct what happened?
Security
Can attackers manipulate the AI?
Human oversight
When should a human take over?
Data governance
Which data can the AI access and use?
These questions are becoming central to the future of Agentic AI.
The GFF 2026 Perspective: Intelligence + Programmability + Trust
One of the most interesting aspects of GFF 2026 is that Agentic AI was not presented in isolation.
The event's broader framework connects three technologies:
Agentic AI + Tokenisation + Quantum
GFF describes these as three pillars moving financial systems toward ecosystems that are autonomous, programmable, and secure.
This creates a fascinating future architecture.
Agentic AI
Provides intelligence.
Tokenisation
Provides programmability.
Quantum technologies
Strengthen security, optimisation and computational capabilities.
Together, these technologies could influence the architecture of future financial systems.
What Will the Future Bank Look Like?
The bank of the future may not look dramatically different from the outside.
Customers may still use a mobile application.
They may still have a bank account.
They may still make payments and apply for loans.
But underneath the interface, the financial institution could become much more intelligent.
The future bank could have AI agents working across:
Customer service
Lending
Payments
Fraud
Compliance
Wealth management
Insurance
Operations
Cybersecurity
Risk management
Humans would remain responsible for strategy, governance, complex decisions, and accountability.
AI would increasingly handle information-heavy and workflow-intensive tasks.
What GFF 2026 Tells Us About the Future of Agentic AI
The most important takeaway from Global Fintech Fest 2026 is that Agentic AI is moving beyond the conversation about productivity.
The bigger opportunity is financial-system transformation.
The question is no longer simply:
How can financial institutions use AI?
It is becoming:
How should financial institutions be redesigned when intelligent AI agents become part of their operating infrastructure?
That is a much bigger question.
And it could influence the future of banking, payments, lending, insurance, wealth management and financial inclusion.
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