A New Phase of Artificial Intelligence in Financial Services 

In recent years, financial institutions have leaned heavily on generative AI (GenAI) and machine learning to streamline back-office operations, automate reporting, and improve the customer experience. But as adoption matures, a new evolution in artificial intelligence is emerging — one that goes beyond producing content or insights to enable autonomous decision-making across complex workflows. 

This next phase is known as agentic AI — a model where AI agents can reason, plan, and act with minimal human intervention. These autonomous systems can execute defined tasks across platforms, access data sets in real time, and collaborate with other agents through secure APIs. They extend what AI chatbots and rules-based automation can do, bringing autonomy to areas like fraud detection, risk assessment, and loan approvals. 

For banks and other financial services providers, the implications are profound: greater efficiency, smarter decision-making, and the ability to optimize performance at scale — all within strict regulatory compliance guardrails. 

From Generating Answers to Executing Actions 

In traditional automation, humans direct every step of the process. Generative AI advanced this by generating content, recommendations, and analysis. Agentic AI systems take it a step further — embedding intelligence directly into functions like onboarding, underwriting, and Know Your Customer (KYC) verification. 

According to research from Deloitte, these agentic solutions allow autonomous agents to reason and act across multiple systems — improving accuracy, speed, and risk management without removing human oversight. In fact, JPMorgan’s Legal Agentic Workflows (LAW) system has demonstrated over 90% accuracy in processing complex legal documents using large language models and domain-specific algorithms — a clear example of agentic AI in banking delivering measurable value in a real-world environment. 

By reducing dependency on manual review and robotic process automation, financial institutions can shift talent toward higher-value strategy and supervision — where people define the guardrails, and AI executes within them. 

Why This Matters: Efficiency, Identity, and Trust 

Agentic AI introduces new use cases that improve operational efficiency and transparency while reinforcing digital trust. It’s not about replacing people — it’s about letting AI-driven systems handle repeatable, data-intensive work so humans can focus on strategy, ethics, and innovation. 

Efficiency and Optimization: AI agents can analyze market conditions and execute decisions across high-risk portfolios or liquidity operations faster than traditional systems. 

Identity and Access: Banks already serve as custodians of the world’s most trusted credentials. Strengthening digital identity, permissions, and zero-trust architectures ensures security even as customers’ bots begin making financial moves autonomously. 

Customer Engagement: AI agents can improve customer interactions by resolving service requests or tailoring financial products in real time, deepening loyalty through more personalized, proactive service. 

As Forbes contributor David Birch notes, when AI tools and bots begin handling financial decisions on behalf of customers, banks will compete less on branding and more on efficiency, transparency, and trust frameworks that enable safe, autonomous systems to operate securely within compliance boundaries. 

How Financial Institutions Can Adopt Agentic AI Securely 

Adopting agentic AI doesn’t mean overhauling every system overnight. Financial institutions can take a phased approach that balances innovation with risk and governance: 

Smart Overlay: Add AI agents to existing workflows — such as KYC updates, credit card fraud checks, or policy monitoring — to reduce manual workloads and errors. 

Agentic by Design: Develop modular agentic AI systems built to handle complex workflows with embedded intelligence and data integration from the start. 

Process Redesign: Reimagine how departments operate by weaving autonomy directly into the ecosystem, aligning AI models, automation, and data flow across functions. 

Each step should include risk management, change management, and continuous validation. Standards such as the Model Context Protocol (MCP) and secure API architectures enable agents to interact across systems safely, while maintaining full traceability and audit trails. 

Governance and Control: The Foundation of Responsible AI 

For any AI-driven initiative, success depends on control and accountability. The most advanced agentic AI uses in banking embed regulatory compliance into the design itself — integrating explainability, human oversight, and auditability from day one. 

BizTech highlights some key requirements for secure AI adoption: 

Robust data governance with version tracking for all data sets and algorithms. 

Embedded permissions, access controls, and segmentation of sensitive information. 

Human-in-the-loop checkpoints for high-impact decisions. 

Alignment with global frameworks like GDPR, FINRA, and the Gramm-Leach-Bliley Act. 

Embedding these guardrails ensures that agentic AI operates within clear limits — enabling speed and autonomy without sacrificing transparency or compliance. 

UDT is Securing the Future of Intelligent Automation 

UDT helps financial institutions modernize their technology landscape to support AI solutions that are secure, compliant, and scalable. 

Our experts guide organizations through the practical steps of AI transformation — from building the infrastructure for large-scale automation to implementing zero-trust architectures and secure AI ecosystems. We work with industry-leading providers to integrate AI models, enforce risk management, and ensure that every deployment adheres to the highest standards of governance and data protection. 

UDT enables your teams to safely roll out AI solutions using an approach that prioritizes efficiency, ensures every decision is traceable, and aligns innovation with your business goals and compliance requirements. 

The Next Chapter in Banking AI 

The shift from generative AI to agentic AI marks a pivotal moment for financial services. It’s not just about smarter AI tools — it’s about autonomous systems that can act responsibly within defined parameters to enhance security, efficiency, and the customer experience. 

With the right strategy, infrastructure, and safeguards, financial institutions can move from reactive automation to proactive intelligence — turning innovation into a long-term competitive advantage. 

Ready to explore how your organization can securely accelerate AI adoption across your enterprise? Contact UDT today to get started.