Why Financial Firms Are Betting Big on Agentic AI?

​In the 2025 financial regulatory landscape, "compliance" is no longer just a buzzword - it has become a core challenge spanning technology, organizational structure, and strategic decision-making. From the increasingly stringent MiFID II regime in Europe​ to Australia's dual-track model of "conduct + structure" regulation​, brokers and financial institutions face a shared dilemma: compliance costs are no longer marginal - they are a structural burden that can determine a firm's survival.

Traditional compliance approaches - such as manual verification, team expansion, or outsourcing - may offer temporary relief. But when dealing with ever-changing regulations, cross-border standards, and technical complexity, these methods fall short. Talent attrition or data errors can easily trigger a vicious cycle of "fix–penalty–refix." This context underscores why the industry urgently needs a new solution.

How Agentic AI Is Reshaping Risk and Compliance

According to a recent study by Fenergo, 93% of compliance, risk, and technology executives at 90 U.S. and U.K.-based financial institutions - including asset managers, investment banks, and commercial banks - plan to deploy agentic AI within the next two years. Six percent have already done so. Agentic AI is evolving from a mere data-processing tool into an "intelligent agent" capable of decision-making and execution, and is emerging as a key disruptor in compliance operations.

This form of AI can understand regulatory intent and autonomously execute task chains within predefined boundaries. It enables end-to-end compliance workflows - from data validation to report generation, risk alerts, and document interaction - bridging the gap between detection and response.

For example, traditional trade reporting often involves multiple teams manually cross-checking fields across systems. Agentic AI, by contrast, can "understand" semantic differences across regulations, automatically align field definitions, detect inconsistencies, and initiate iterative corrections. This perception–decision–feedback loop far surpasses early RegTech systems based mainly on static rules.

Moreover, Agentic AI can adapt to evolving regulatory standards - such as the frequent updates to MiFID II's RTS - by modifying its own models or prompting rule revisions, making "sustainable compliance" achievable. This is particularly valuable when dealing with MiFID II's 30,000 pages of documentation - a scenario where speed and security are critical.

According to Fenergo's study, 93% of executives plan to adopt this technology within two years, with 6% already doing so.

The "AI Dividend" in the Eyes of Financial Institutions

Financial firms are prioritizing Agentic AI in high-risk, high-value scenarios, with fraud detection being the most common application, followed by customer KYC maintenance and transaction surveillance. These areas involve high data volumes, complex processes, and tight response deadlines - conditions under which AI can demonstrate its decision-making agility and rapid response capability.

Unlike static rules engines, Agentic AI can not only identify problems but also propose solutions and even initiate responses. For instance, when potential fraud is detected, it can generate reports, trigger internal investigations, or send verification requests to customers - dramatically compressing reaction times and boosting operational efficiency.

But the industry's expectations go beyond efficiency gains. Agentic AI is also seen as a powerful lever for cost optimization. Fenergo's findings suggest that 26% of respondents expect the technology to reduce annual compliance costs by over $4 million. This cost-saving potential stems from three key factors:

• ​Automation replaces labor-intensive manual reviews, freeing up human resources;

• ​Intelligent decision-making accelerates case resolution and approvals;

• ​Reduced compliance violations help avoid hefty fines and reputational damage.

Toward a Human-AI Collaborative Compliance Model

Despite its promise, Agentic AI presents regulatory risks that cannot be ignored. In the U.S., 44% of financial institutions cited data privacy as a concern, while 36% pointed to regulatory uncertainty.

The "black box" nature of AI decision-making remains problematic. When errors occur, issues of accountability, audit trails, and explainability become critical. In addition, jurisdictional differences in how AI is regulated and perceived make global deployment more complex.

It's also important to note that Agentic AI cannot fully replace human judgment or ethical oversight. Organizations like the International Compliance Association (ICA) and FINRA emphasize that data quality, model governance, and audit transparency remain key barriers to adoption. The future, therefore, is not one where AI replaces humans, but one where humans and machines work in tandem - a collaborative compliance paradigm.

In summary, as AI evolves from an "assistive tool" to an "intelligent compliance agent," it is quietly reshaping the technological foundations of the financial sector. Those who master this shift early will gain a strategic edge in the increasingly competitive compliance arena.