The rise of Agentic AI is making waves in the financial services industry, and India is at the forefront of this transformation. As we witness increasing operational inefficiencies in banks and financial institutions, Agentic AI is emerging as a game changer, revolutionizing how financial services are delivered. In this blog post, I want to break down how Agentic AI works, why it’s vital for the future of financial services, and why India is leading the charge in this space.
Why Agentic AI is Creating Ripples in Financial Services
Over the years, financial institutions—especially banks—have been struggling with mounting operational costs. Based on my experience with recent RFPs, it’s evident that business processing in banks accounts for 40%-60% of these costs, which directly impacts the customer experience. Take, for example, the KYC (Know Your Customer) verification process. Typically, it takes anywhere from 3 to 7 days due to manual intervention. Such delays not only reduce productivity but also affect customer satisfaction. And with increasing regulatory scrutiny, the costs associated with lapses in compliance have never been higher.
While Robotic Process Automation (RPA) has played an essential role in streamlining basic workflows, it still faces limitations, especially when it comes to handling exceptions. The result? Increased manual oversight, which drives up costs and reduces efficiency. This is where Agentic AI comes into play, offering a breakthrough in terms of autonomous decision-making, optimizing costs, and improving overall accuracy.
How Agentic AI Helps
Unlike traditional automation, Agentic AI isn’t just about executing predefined tasks. It’s about reasoning, learning, and adapting—allowing the system to evolve based on data and new insights. For instance, in KYC verification, Agentic AI systems can automatically request additional documents or follow-up actions based on specific criteria, whereas manual operators would only detect these discrepancies and initiate follow-up actions later.
By structuring AI agents to work collaboratively, financial institutions can streamline their operations, handle edge cases dynamically, and create self-optimizing workflows. This leads to significant improvements in processing speed, accuracy, and overall customer experience. In essence, Agentic AI is pushing the boundaries of what’s possible in automation and turning mundane, repetitive tasks into intelligent processes that continuously improve over time.
The Role of Small Language Models (SLMs) in Enhancing Agentic AI
The power of Agentic AI is amplified when combined with Small Language Models (SLMs). These models are fine-tuned for specific banking tasks, allowing them to perform with higher accuracy and faster processing times than generic AI systems. SLMs enhance the contextual awareness of Agentic AI, enabling it to continuously learn from new regulations, evolving fraud patterns, and customer interactions. This makes Agentic AI much more dynamic, adaptable, and capable of responding to the ever-changing landscape of the financial services sector.
Think of it as a specialist like Jasprit Bumrah in cricket—his focus and precision in death overs is unmatched. Similarly, SLMs are optimized for banking tasks, ensuring that Agentic AI remains sharp and precise in managing complex financial tasks.
The Future: Where Are We Headed?
The future of Agentic AI in the financial services sector looks bright. As AI technologies continue to evolve, Agentic AI will become even more autonomous and self-improving. With the advent of AI engines such as DeepSeek, Grok, Gemini, Mistral, Claude, and Llama, the capabilities of Agentic AI in reasoning and adaptability are growing exponentially. These AI systems aren’t just executing tasks—they are thinking, learning, optimizing, and improving in real-time.
This is particularly important as banks and financial institutions increasingly look for AI-driven solutions to streamline Business As Usual (BAU) processes. Customer interactions are becoming more intelligent and self-regulating, and banks need to ensure they’re equipped to handle this shift in customer expectations. The question is no longer if AI will take over critical banking functions, but how quickly will financial institutions adopt these systems to remain competitive?
India Leading the Charge
India has been at the forefront of AI adoption in the financial services sector, and it continues to be a global leader in harnessing the power of Agentic AI. According to a recent Deloitte report, 80% of businesses globally are actively exploring the development of autonomous agents, with India being a major player in this shift. The country’s robust tech ecosystem, talent pool, and increasing focus on financial inclusion position it as a hub for Agentic AI innovation.
Indian banks and financial institutions are already integrating AI-driven solutions to enhance operational efficiency, reduce costs, and improve customer service. Furthermore, with a growing focus on data privacy and compliance, Agentic AI’s ability to adapt and stay aligned with regulatory requirements makes it a natural fit for the Indian market.
Conclusion: The Role of Agentic AI in the Global Banking Sector
Agentic AI has the potential to redefine financial services just as RPA did in the past. The global banking sector must explore this technology actively to stay ahead of the curve. With AI models becoming smarter and more efficient, financial institutions that fail to adopt Agentic AI may soon find themselves at a competitive disadvantage.
I encourage fellow industry experts to share their insights and experiences as we continue to learn from each other. The future of banking is here, and it’s powered by Agentic AI. Let’s explore this exciting frontier together!
Thanks for reading.
