Agentic AI in finance: Why autonomy, not just automation, is the next frontier

The financial sector has historically been a zone of innovation (double-entry accounting, algorithm-based trading)—it continuously pushes the boundaries of what is technologically possible. Today, we are at an inflection point that moves beyond the automation of tasks into a real change in reasoning and decision-making. This is agentic AI.

It is different from conventional AI, which follows instructions, because agentic AI can independently perceive, decide, and act. Agentic AI will function as an autonomous actor, instead of a passive instrument. In financial services, this will fundamentally redefine the operations of financial services across functions from trade execution to risk management—it represents a fundamental change in the way we use intelligence across the industry.

Rise of autonomous trading

Speed has certainly been an edge in finance, but it is no longer enough. We have a new focus on intelligent speed—machines are responding in milliseconds, while gathering information in real time, and learning.

This is where agentic AI is making great strides. AI-enabled trading systems can execute trades autonomously at speeds that simply can't be matched. Speed alone does not matter; what is revolutionary is that they can also aggregate and analyse multiple data streams in real time—price movements, trading volume, news sentiment, and geopolitical signals—into decisions they can act on. The term high-frequency trading is a part of evolving into autonomous systems, or intelligent trading systems, especially in finance.

Rethinking risk: From reactive to proactive intelligence

Risk management has traditionally been based on human analysts interpreting signals from spreadsheets and static models, but with markets currently moving in milliseconds and the amount of data flowing growing exponentially, that model is fading fast. Agentic AI is a new model; it will ingest all levels of structured and unstructured data (news, social media, filings), among others, and establish risk before it emerges. It not only flags the anomaly but also understands the anomaly in its context and recommends action.

And this paradigm shift won't be limited to topology or institutions. The rise of sentiment analysis is paving the way for the average individual to have sentiment and insights that were only previously available to a hedge fund. Agentic AI can assess public sentiment (from social media sites like Twitter and Reddit) to a specific stock or sector, indicating risk awareness, possibly before a quarterly report identifies such acts of management. This distribution of intelligence may prove to be agentic AI's most disruptive and exciting impact.

Tools powering the transition

What is making this shift possible is a new array of platforms and technologies that can operationalise and scale agentic AI.

• Cloud platforms like Microsoft Azure deliver the compute power to process massive data sets in real-time.
• Many natural language processing (NLP) models, including OpenAI’s GPT, are being fine-tuned for financial contexts, allowing for more nuanced sentiment analysis, market prediction, and risk modelling.
• Platforms like Alpaca and QuantConnect allow traders to build, test, and deploy AI trading algorithms at a level not requiring a PhD in machine learning.

These tools are democratising, further empowering smaller firms and retail investors with the same intelligence that powers Wall Street.

Autonomous finance and the ethics of delegation

As agentic AI accelerates, we are also advancing toward a time where financial systems are not only automated but wholly autonomous. Imagine hedge funds run with minimal human involvement, and that those systems will learn from market behaviour, and optimise and outflank human strategies on the fly. Imagine risk management systems that can identify malign threats to your company and neutralise them before they ever come across the desk of an analyst.

However, with power comes responsibility, and there will come a new set of ethical and governance issues.

• Who is accountable when an autonomous system makes an unintended error?
• How do we maintain integrity and transparency throughout a decision-making process that is fully hidden?
• What happens to human cognition when we trust a machine to act independently on our behalf?

Technology has evolved to the point where we could pass a starkly anti-democratic action known as autonomous AI.

Final reflections: Embracing the shift

It’s not a long-ago vision of the future and is already bringing about change in the core of financial services. It can be faster, bigger, smarter—at scale—than we could rightfully imagine even a decade ago. But most fundamentally, it offers a new way to think about systems—multi-agent systems, adaptive systems, decentralised systems, contextual systems.

We in the industry can either decide to stay in this place of resistance, refusing to be unchained from past models and manual processes, or we can choose to accept this new paradigm—thoughtfully, ethically, and strategically. The future of finance will not be built by those who automate the past; it will be built by those who can imagine what is possible.

(Sundaravaradan Ravathanallur Chackrvarti is Vice President, Principal Data Architect at U.S. Bank.)