Artificial intelligence has long ceased to be a novelty in the financial sector. For at least the past decade, banks, insurers, and fintechs have incorporated machine learning models and advanced analytics. Among other use cases: detecting fraud, optimizing credit scoring, automating processes, and improving customer experience. But now a new stage begins: that of Agentic AI.

A revealing fact: market consultancy Mordor Intelligence states that investment by companies in this sector in this technology will grow at the impressive rate of 41% annually between 2026 and 2031.
Traditional approaches focused on recommending or assisting. Intelligent agents, by contrast, are systems capable of planning, coordinating, and executing actions autonomously within complex workflows. They go far beyond answering queries or classifying data: they interact with multiple systems, make bounded decisions, and advance processes end-to-end.
In an industry where speed, accuracy, and regulatory compliance are critical, this paradigm shift opens powerful and at the same time challenging opportunities.
Intelligent agents as a strategic necessity
In financial services, the appeal of Agentic AI’s proposition is immediate.
Companies in the sector are characterized by pressure to reduce operating costs, accelerate response times, and deliver hyper-personalized experiences. This coexists with a demanding regulatory environment and historically fragmented technological infrastructures.
In this context, the possibility of having agents that automate complex processes and coordinate decisions in real time begins to be seen as more than a competitive advantage: as a true strategic necessity.
Use cases for banks and fintechs
Use cases are multiplying.
- Fraud prevention: Agents capable of monitoring transactions, correlating signals, escalating alerts, and activating automatic blocks reduce detection times and minimize losses.
- Regulatory compliance and AML: Systems that orchestrate evidence collection, identity validation, and report generation improve traceability and ease operational burdens.
- Credit: Agents that combine structured and unstructured data accelerate evaluations, propose dynamic limits, and accompany the entire origination cycle.
Applications also appear in customer service and internal operations. Agents that:
- Resolve complex claims.
- Coordinate back office and front office.
- Assist teams in reconciliation, reporting, and accounting close tasks.
In all cases, the greatest value does not arise from automating isolated tasks, but from redesigning complete processes end-to-end, reducing friction and freeing talent for higher-impact activities.
Challenges, risks, and complexities
The leap toward agentic models introduces a new dimension of risk and complexity.
When systems decide and act, critical issues of governance, control, and responsibility come into play:
- What can an agent decide and what must be escalated to a person?
- How are its actions audited?
- How is traceability ensured in highly automated environments?
In a sector under constant supervision, these questions are central.
Therefore, the adoption of Agentic AI in finance must be approached with vision, orchestration frameworks, governance policies, and integration with core systems. Architecture, security, and compliance cease to be subsequent layers and become design principles from the outset.
Nubiral: Concrete approach to Agentic AI in the financial sector
At Nubiral we address this challenge with an AI and GenAI strategy governed from the start, conceived as a platform rather than isolated projects. The focus is on standardizing development through a unified GenAI platform. This includes reusable components, clear security guidelines, and a centralized governance model that allows teams to innovate without losing control.
Specifically, in the financial sector we work with organizations facing an increasingly common scenario:
- A fragmented technology stack.
- Multiple disconnected AI initiatives.
- Slow implementation processes and teams with mostly legacy skills.
- Regulatory pressure, security requirements, and the need to scale without compromising compliance or traceability.
The proposal includes an internal marketplace of components, accelerators, and reusable patterns, along with a progressive upskilling plan for teams. This reduces dependence on scarce talent and accelerates time-to-market.
In this way, AI ceases to be a one-off experiment and becomes a transversal business capability—governed, scalable, and aligned with the strategic objectives of the financial sector.
Conclusions
This approach enables financial organizations to advance in the adoption of Agentic AI in a secure, scalable, and sustainable way. It connects business, technology, and governance within a single architecture.
Entities that integrate intelligent agents with this long-term vision will be better prepared to compete in an environment where efficiency, trust, and resilience are decisive factors.
Is your organization looking to adopt intelligent agents with a strategic perspective? We have a team of experts ready to help you. Schedule your meeting today!
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