The rise of agentic AI in finance: Streamlining operations with finance automation

In a time where financial institutions (FIs) face mounting pressure to reduce costs, accelerate operational cycles and manage risks more proactively, the rise of agentic AI presents an opportunity. Unlike traditional automation, AI agents can act, reason, and adapt.

Here, we’ll explore how agentic AI is reshaping the operational backbone of banking and fintech, and how FIs can adopt it safely and strategically.

What is agentic AI, and why does finance need it now?

Agentic AI refers to a new class of artificial intelligence (AI) systems designed to operate with autonomy and adaptability, making them particularly relevant for today’s financial services landscape. Such agentic systems use natural-language capabilities to plan actions, leverage online tools to complete tasks, and collaborate seamlessly with other agents and people - all while continuously learning and improving performance.

As banks and fintechs face tightening regulatory requirements, rising data volumes, and complex operational processes, agentic AI offers a way to move beyond static automation toward dynamic systems that can reason, plan, and act in real time while remaining aligned with business and compliance objectives.

For FIs still reliant on labor-intensive, manual processes, particularly in credit assessment and lending execution, this finance automation technology is a game-changer. It drives end-to-end productivity gains, reducing friction across processes. According to research by McKinsey, agentic AI can produce high-quality content and reduce review cycle times by up to 60% when compared to traditional AI architectures.

Making agentic AI real: from strategy to execution

Transforming agentic AI from a strategic concept into an operational reality requires a phased approach that balances innovation with control.

FIs should start by piloting high-impact, low-risk use cases to demonstrate value quickly. Adopting modular architectures and building a robust data foundation enables agentic AI agents to integrate seamlessly with core banking systems. At the same time, institutions must codify processes into structured workflows that are then used to train these intelligent agents.

Strong governance and comprehensive human oversight are other important factors to consider. Compliance-driven integration requires feedback loops and approval checkpoints to ensure accuracy, transparency, and audibility at every stage. These safeguards not only mitigate risk but also build trust in AI-driven decision-making.

Operating safely: risk, governance, and trust in agentic AI

As AI agents gain the ability to act autonomously within financial systems, risk management, and regulatory alignment must sit at the heart of every operating model.

Without robust governance, AI can introduce compounding operational risks, eroding customer trust, creating a workforce resistant to AI adoption, and compromising data security, which invites regulatory scrutiny and legal liability. For example, under the EU AI Act, organizations face penalties of up to €35 million or 7% of global annual turnover for prohibited AI practices.

To operate safely and efficiently, technology leaders have a responsibility to first understand the risks inherent in autonomous agents and workflows before moving to implementation. Throughout this journey, the following guiding principles are critical:

• Ensure the explainability and auditability of agent decisions to enable accountable workflows and build trust.
• Upskilling the workforce so employees can confidently adopt and collaborate with intelligent platforms.
• Maintain rigorous data quality standards, embed compliance-by-design principles, and update AI policy frameworks and risk programs. Role‐based access controls (RBAC) are pivotal.
• Monitor performance, vendor dependencies, and model changes to preserve trust with regulators, customers, and internal stakeholders.
• Stay ahead of regulatory changes at both regional and global levels.

Powering agentic AI through strategic fintech partnerships

As agentic AI moves from concept to capability, FIs must leverage the strength of their external ecosystem, especially fintech partners.

Instead of building infrastructure from scratch, institutions can collaborate with fintechs that offer modular, interoperable solutions designed for finance automation and AI-driven compliance. These partnerships accelerate deployment, reduce risk, and enable agentic AI to deliver value faster.

Finastra plays a key role in this evolving landscape by helping its clients embed agentic AI into systems and workflows. Through open APIs, robust developer tools and a shared data foundation, Finastra supports the creation of intelligent systems that act autonomously while remaining aligned with compliance frameworks.

Finastra’s Lending AI solutions empower institutions to integrate AI intelligence into credit workflows, reducing manual intervention and improving productivity across lending operations. By embracing strategic partnerships and collaborative innovation, FIs can move agentic AI out of the lab and into production.