Dhivya Nagasubramanian, Vice President of AI Transformation & Innovation at a Fortune 500 Company.
The financial services industry stands on the brink of a technological revolution. While AI has already transformed areas like algorithmic trading and fraud detection, a new wave is emerging: agentic AI. This advanced form of AI doesn’t just process data—it makes autonomous decisions, learns from experiences and interacts with the environment in real time.
According to Citi’s January 2025 report, “Agentic AI: Finance & the ‘Do It For Me’ Economy,” this technology could have a more profound impact on the economy and finance than the internet era itself.
What Is Agentic AI?
Agentic AI systems are able to make decisions independent of human intervention. These AI agents can proactively solve complex problems, adapt to changing circumstances and execute tasks across various domains. Unlike traditional AI, which requires human prompting, agentic AI operates autonomously, making it a powerful tool for industries like finance.
The Evolution Toward Agency
What sets agentic AI apart is its ability to operate with a degree of autonomy that was, until recently, the realm of science fiction. These systems can plan, reason, reflect and even initiate tasks without waiting for human direction. They are not simply analytics engines—they are actors.
In the context of finance, this evolution is more than a technological upgrade—it’s a structural shift. Think about the difference between a calculator and a CFO. The former computes; the latter strategizes. Agentic AI blurs that line.
A Quiet Revolution In Financial Services
Here’s how agentic AI is starting to make its presence felt:
Autonomous Portfolio Management
Agentic AI makes it possible to continuously monitor market signals and adjust asset allocations in real time, and it does so based on an investor’s evolving risk tolerance and goals. This is made possible not with rule-based algorithms, but with adaptive, long-horizon decision making.
Dynamic Risk Modeling
Risk is no longer a static calculation. Agentic AI can ingest macroeconomic data, geopolitical developments and market microstructures, synthesizing them into live scenario modeling. It doesn’t just report risk—it mitigates it by proactively rebalancing exposure across portfolios or recommending hedges.
Proactive Fraud Detection
Today’s fraudsters use AI. Tomorrow’s fraud fighters will, too. Agentic AI systems can detect anomalous behavior patterns, trace potential threats across systems and even initiate preventive actions—like freezing transactions or launching internal audits—before humans are even aware there’s a problem.
Compliance-As-A-Service
With regulatory frameworks in constant flux, compliance is a moving target. Agentic AI can monitor regulatory updates, interpret legal language and reconfigure internal protocols to maintain compliance—all autonomously. It has the potential to turn a costly, reactive function into a streamlined, proactive capability.
Personalized Customer Engagement
Agentic AI can engage with customers through chatbots as well as deep contextual understanding. Essentially, financial companions remember long-term goals, anticipate needs and deliver tailored financial advice across digital channels at scale.
The Risks Of Intelligent Autonomy
With great capability comes significant responsibility. Agentic AI poses real challenges—ethical, operational and regulatory. Who is accountable when an autonomous agent makes a bad trade? How do we ensure transparency in systems that “think” in ways even their creators can’t fully predict?
These aren’t academic concerns—they’re live questions for boards, regulators and executives. The rise of agentic AI demands a new governance framework: one that balances innovation with oversight and speed with safety.
For example, a critical starting point is an agent accountability architecture, which ensures that all agentic actions are traceable and governed by clear lines of responsibility. This includes defining human-in-the-loop roles, establishing escalation protocols for high-risk decisions and assigning unique digital identities to each agent. All agent activity should be logged through immutable audit trails containing contextual metadata—capturing not just what the agent did, but why.
Equally essential is defining autonomy boundaries and delegated authority. Financial institutions must implement policy-based constraints that outline the precise scope of an agent’s autonomous decision making—such as limiting portfolio rebalancing to within 2% unless escalated. A tiered system of intervention—ranging from full autonomy to human override—helps manage this spectrum of control.
Furthermore, agent permissions must be aligned with function, encoded in a role-based autonomy matrix that ensures agents operate strictly within their domain of responsibility. Without these structural safeguards, the risks of intelligent autonomy could far outweigh its benefits.
Human Plus Machine, Not Human Versus Machine
Despite the hype, agentic AI isn’t here to replace financial professionals—it’s here to augment them. The most forward-thinking firms will empower talent rather than sideline it. Routine tasks, data sifting and compliance checks will increasingly fall to intelligent agents, freeing up human experts to focus on creativity, strategy and trust-building.
What we’re witnessing is not just an upgrade in tools—it’s a reimagination of roles. The goal for financial institutions should be to embrace AI as a collaborator, not just a technology.
Final Thoughts
Agentic AI is no longer an experiment. It’s entering the real-world workflows of asset managers, banks, fintech startups and regulators alike. The question isn’t whether finance will adopt agentic AI—but how quickly and with what guardrails.
In a sector built on trust, intelligence and foresight, the ability to deploy autonomous AI agents could become the next major differentiator. As with all transformative technologies, early movers may gain the most—but only if they move wisely.
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