AI already plays a foundational role in banking, enhancing customer experiences and delivering faster, data-driven decisions. However, the next phase of AI goes beyond smarter algorithms or faster processes. Now, agentic AI is emerging – intelligent systems that not only predict outcomes but also act on them autonomously, making decisions in real time and adapting as situations change. Banks in the UK and across the globe are starting to benefit from these systems, which promise greater agility, efficiency, and scalability.
From automation to intelligent agency
To grasp agentic AI’s potential, it helps to look at how banks have used traditional AI to date. Common applications include automating repetitive tasks, monitoring for suspicious transactions, and using chatbots to respond to customer queries. These systems are good at recognising patterns and making predictions, but they usually only offer recommendations or send alerts. A human is still needed to approve or act on their suggestions.
Agentic AI takes the next step: operating within defined parameters, these systems initiate action independently. They can make and carry out decisions independently. For example, in mortgage processing, agentic AI does not simply sort applications, it can extract data, request missing documents, route cases for approval, and update compliance logs. All of this can be done automatically and in real time, with little human involvement.
Beyond the back office
At first, most excitement about AI in banking was around improving back-office processes. Now, agentic AI is also having a visible effect on customer service and staff support. Banks are starting to use AI-driven agents to help both employees and customers. For example, AI copilots can help developers build new banking apps, guide staff through complex compliance tasks, or answer customer questions about products and policies.
What differentiates truly advanced AI agents is transparency, and trust built through explainability. These systems do not just give answers, they also explain their reasoning. This allows users to understand and trust the information they receive. In banking, where trust and accountability are essential, this is particularly important.
Why banking is ready for agentic AI
Banking is a highly regulated and data-rich industry. The sector’s complexity – resulting from layers of compliance, risk management, and customer service – makes it an ideal environment for intelligent automation. Agentic AI helps banks scale decision-making while preserving agility, auditability, and control.
Take fraud prevention, for example. Traditional AI might flag a suspicious transaction and send it to a human for review. Agentic AI can go further by immediately placing a temporary hold on the account, notifying the customer, or escalating the case based on a real-time assessment of risk. Similarly, in credit risk management, agentic AI can continually recalculate risk as new data comes in, adjusting lending limits or recommending action automatically.
Building trust through governance and regulation
As AI systems become more autonomous, greater responsibility is required. In banking, mistakes made by AI can have serious consequences for both individuals and institutions. New regulatory frameworks, such as the EU AI Act, are introducing risk-based rules for AI, with significant penalties for non-compliance.
To earn trust, agentic AI must be transparent, auditable, and governed by human oversight. Every action taken by an AI agent – whether approving a loan or handling a complaint – should be logged and open to review. There must also be clear ways for humans to step in and take control. No AI system should operate entirely without human oversight.
Laying the technical foundations
Bringing agentic AI into banking is not simply a matter of installing new software. It needs a strong technical foundation based on integrated processes, high-quality data, and strict privacy controls.
First, business processes must be redesigned to include both digital and human input. For example, in loan origination, AI can automate document sorting, data extraction, and anomaly detection. However, people must handle complex cases or those that need judgment. AI must operate within modern workflow platforms, collaborating with humans, never replacing them.
Second, access control is crucial. Banks must ensure that only authorised people and systems can access sensitive information. Record-level security is needed to prevent data leaks or misuse, given the large volumes of personal and financial data banks hold.
Third, agentic AI relies on real-time, unified data, making connected data fabrics essential. Modern data management systems help break down barriers between core banking, CRM, compliance, and external sources. With a complete, real-time view of data, AI can make the best decisions.
Where agentic AI is already making a difference
Lending is one area where agentic AI is already having a significant impact. The traditional loan process is slow and involves a lot of paperwork. Agentic AI can automate document sorting, extract key financial details, validate forms, and flag suspicious activity. Every action is logged, which makes compliance easier.
The result? Faster decisions, fewer errors, and a seamless customer experience. Importantly, banks can still allow human staff to step in when needed. This ensures the right balance between efficiency and accountability.
Future opportunities and challenges
As agentic AI continues to develop, its potential goes far beyond making processes more efficient. These systems shift banks from reactive operations to proactive, adaptive decision-making. Imagine AI that autonomously monitors market changes and adjusts risk exposures. Or customer service agents that anticipate problems and solve them before they escalate.
However, this future relies on banks investing in governance and risk-awareness. Agentic AI must be designed with transparency, auditability, and human oversight at its core.
The rise of agentic AI marks a turning point for banking. By moving beyond automation and prediction to systems that can reason and act in real time, banks can unlock new levels of agility, efficiency, and innovation. The challenge now is to use this technology responsibly.
By combining AI autonomy with human oversight, banks can deliver smarter, safer, and more trustworthy services. This will help them keep pace with rapid technological change and set new standards for trust and value in the financial sector.
