Agentic AI enables autonomous reasoning and decision-making in financial services, offering transformative benefits in advisory services, fraud detection, and compliance but requires careful data governance and human oversight.
The financial services industry is on the cusp of a transformative shift with the emergence of agentic artificial intelligence (AI). Unlike generative AI, AI agents don’t require prompts or human direction to perform tasks. Instead, these agents are capable of autonomous reasoning, planning, and decision-making. Agentic AI can take initiative, collaborate with other systems, and act as true digital partners.
As Anshul Gandhi, former senior machine learning engineer at Dell Technologies, observes, “We have already entered the era of agentic AI where systems can reason, plan, and collaborate to act as true partners.” This evolution from reactive to proactive AI systems presents unprecedented opportunities for financial institutions to enhance operations, improve customer experiences, and maintain a competitive edge.
This ability to act autonomously, however, comes with inherent risks that require careful management. First, let’s look at some concrete, near-term use cases for agentic AI within financial services.
Autonomous client advisory services: One of the most promising applications of agentic AI lies in client advisory services. Kumar Srivastava, chief technology officer at Turing Labs, explains that “an ‘advisor’ agent can be on a constant lookout for new opportunities, evaluate them, and present them to the customer for execution.”
This capability elevates traditional financial advisory from periodic consultations to continuous, personalized guidance. Agentic AI advisors can monitor market conditions, assess individual client portfolios, and proactively identify investment opportunities or risks that align with specific client goals and risk tolerances. The result is a more responsive, personalized advisory experience that operates around the clock.
Intelligent risk management and portfolio optimization: Complementing advisory services, agentic AI excels in autonomous risk assessment. Srivastava highlights the potential for a “risk” agent that can be “deployed to automatically and constantly assess portfolio risk and determine risk mitigation and rebalancing opportunities.”
This continuous risk monitoring represents a significant advancement over traditional periodic reviews. Agentic AI can analyze market volatility, geopolitical events, and individual portfolio performance in real-time, automatically suggesting or even executing rebalancing strategies within predefined parameters. This capability not only enhances risk management but also ensures optimal portfolio performance across varying market conditions.
Advanced fraud detection and response: Vivek Singh, senior vice president of IT and strategic planning at PALNAR, points to “real-time fraud detection” as a key near-term application. Agentic AI systems can autonomously monitor transaction patterns, identify suspicious activities, and take immediate protective actions without waiting for human intervention.
Joan Goodchild, founder of Cyber Savvy Media and cybersecurity journalist, expands on this concept: “On the back end, agentic AI can not only detect anomalies, but autonomously investigate them and even trigger workflows to freeze accounts or notify customers in real time.” This autonomous response capability can significantly reduce the window of vulnerability in fraud scenarios, potentially saving millions of dollars in prevented losses.
Automated compliance and regulatory monitoring: In the heavily regulated financial services environment, agentic AI offers substantial value in compliance monitoring. Singh identifies “regulatory compliance monitoring” as a critical near-term use case, while Gandhi points to “automated compliance” as a key application.
Scott Schober, president and CEO at Berkeley Varitronics Systems, Inc., sees particular value in “cutting down on the manual work of compliance checks.” Agentic AI can continuously monitor transactions, communications, and activities against regulatory requirements, automatically flagging potential violations and initiating corrective actions or reporting procedures as needed.
Intelligent credit evaluations: Gandhi also highlights “intelligent credit decisioning” as a transformative near-term application. Agentic AI can autonomously evaluate loan applications by analyzing multiple data sources, assessing risk factors, and making lending decisions within established parameters. This capability can dramatically reduce approval times while maintaining or even improving decision accuracy.
Enhanced customer service automation: Agentic AI can also enable more sophisticated customer service capabilities. Goodchild describes “virtual financial assistants that move beyond chatbots to proactively resolve account issues, automate routine transactions, or offer tailored investment advice.”
Arsalan Khan, speaker, advisor, and blogger, identifies “streamlining customer service” as a key near-term opportunity, emphasizing how agentic AI can provide more contextual, personalized responses while handling complex multi-step customer requests autonomously.
Agentic AI implementation: Data governance and a measured approach
The success of agentic AI implementations hinges critically on data quality and governance. Khan provides a stark warning: “Fragmented, incomplete, or context-poor data will turn your AI from a game-changing enabler into a costly obstacle. Without the right data foundation, AI becomes a hindrance rather than a source of convenience and competitive advantage. Bad data will turn AI from a competitive weapon into a liability.”
This insight highlights the importance of establishing robust data governance frameworks prior to deploying agentic AI systems. IT leaders must ensure data quality, completeness, and contextual richness across all systems that will feed into agentic AI applications.
Every expert interviewed for this article emphasizes the importance of measured deployment approaches. Singh advises IT leaders to “start with controlled pilots,” while Schober recommends “starting small, testing in controlled environments.” Gandhi echoes this sentiment: “My advice to IT leaders is to pilot these systems in controlled domains.”
This conservative approach allows organizations to understand system behavior, identify potential issues, and refine implementations before scaling. Controlled pilots also provide valuable learning opportunities for both technical teams and end users.
Maintain human oversight and transparency
Despite their autonomous capabilities, agentic AI systems still require careful human oversight, particularly in a highly regulated industry such as financial services. Singh emphasizes the need to “ensure human-in-the-loop oversight,” while Schober stresses the importance of “always keeping human oversight in place.”
Goodchild specifically addresses the regulatory implications: “Deploying autonomous agents in a highly regulated industry means transparency, auditability, and human oversight are non-negotiable.” This requirement extends beyond simple monitoring to include comprehensive audit trails and explainable decision-making processes.
Singh recommends that IT leaders “strengthen data governance and build transparent, auditable decision logs.” This advice reflects the critical need for comprehensive monitoring systems that can track AI decision-making processes and outcomes.
Schober emphasizes that “strong monitoring and regular audits are critical so the benefits are realized without opening the door to new problems, whether that’s bad data, hidden bias, or challenges tying into existing systems.”
As Goodchild concludes, “Done right, agentic AI can enhance both customer experience and risk management, but only if IT ensures safety and accountability are built in at every step.” Organizations that successfully navigate the balance between innovation and control will find themselves at a significant competitive advantage in the evolving financial services landscape.
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