Agentic AI is fundamentally transformative, and when paired with AI assistants, it facilitates the overhaul of antiquated workflows to provide seamless, user-friendly experiences that revolutionize business processes. Its remarkable abilities in autonomous or semi-autonomous decision-making, adaptability, and proactive problem-solving have already begun to streamline approvals, remove bottlenecks, and enable banking professionals to focus more on strategic decision-making.
Agentic AI: The Upcoming Frontier in Banking
Standard AI models operate solely on prompts, while Agentic AI makes independent choices and takes meaningful actions to meet specific objectives without needing explicit guidance. Rather than just producing fixed outputs, Agentic AI takes instructions, formulates a thoughtful plan, and utilizes various tools to accomplish tasks while generating dynamic responses tailored to the nuances of each situation.
Agentic AI represents a compelling new way to work, as it can perceive, think, and operate independently without requiring manual intervention.
According to Gartner, by 2028, 33% of enterprise software applications will incorporate Agentic AI, a significant increase from less than 1% in 2024. This will allow 15% of daily work decisions to be made autonomously. The expanding availability of AI tools holds enormous potential to reshape the banking sector within the next two to three years. Analysts suggest that the banking and financial services industry will invest over $10 billion in developing and implementing AI-driven banking solutions. Additionally, Deloitte anticipates that by 2027, half of the companies utilizing generative AI will have launched Agentic AI.
The pace at which Indian banks adopt Agentic AI will greatly affect their competitive edge in the digital economy. Strategically employing AI agents and assistants could revolutionize banking operations by boosting autonomy, efficiency, and adaptability in their business processes. A recent study reveals that banks early to embrace Agentic AI have experienced a reduction in Mean Processing Time by as much as 50% to 90%, alongside a complete eradication of non-compliance risks.
From more efficient loan approvals and faster fraud detection in retail banking to the automation of manual trade finance tasks in corporate banking, AI agents will play a significant role in digitization, boosting efficiency, delivering personalized financial services, and providing 24/7 customer support.
Here are several ways banking operations will evolve with AI agents in the future:
- Automated Account Reconciliation RPA bots: These will automate daily reconciliations by extracting transaction data, comparing it, identifying discrepancies, and generating reports at a significantly faster pace.
- Deterministic AI Agents for fraud detection: A deterministic AI agent will evaluate real-time transaction data, employing rules and Machine Learning models to detect fraud, flagging suspicious transactions for human review.
- Personalized Financial Advice: AI Agents will assess a customer’s situation and recommend strategies. They will learn, adapt, and may even execute actions (with proper permissions).
For instance, IBM recently partnered with a global bank to launch an Agentic App for onboarding commercial bank clients, reducing onboarding time by nearly 60% and rework by 90%.
Establishing the Framework for Responsible Agentic AI in Banking
Businesses must create a framework for an Agentic AI architecture that is both modular and scalable, allowing new agents and functionalities to be integrated seamlessly without disrupting existing workflows. This modularity will support adaptability in dynamic environments, ensuring the system can grow and evolve efficiently.
Furthermore, effective communication between agents is essential, utilizing standardized messaging protocols to enable agents to share information, coordinate actions, and maintain interoperability across various components.
The framework should also implement task granularity by breaking complex tasks into simpler, manageable components, allowing specialized agents to focus on specific areas, thereby improving overall efficiency and accuracy.
Finally, robust security and governance must be integral to the design, safeguarding the vast amounts of data managed by the system. These measures will protect against unauthorized access, data breaches, bias, and model drift while ensuring user privacy, making the architecture responsible and suitable for handling sensitive information in Agentic AI systems.
— Geeta Gurnani is IBM Technology CTO & Technical Sales Leader, India & South Asia.
