Artificial intelligence has supported banking operations for years. It has found many applications in fraud detection, credit scoring, risk modelling, and customer service automation. In 2026, however, industry discussions have increasingly focused on agentic AI — a new category of proactive AI systems designed to plan, coordinate, and execute multi-step tasks instead of simply generate predictions or respond to prompts.
The concept has gained prominence not just due to its novelty or hype, but because financial institutions have been long looking beyond isolated AI applications toward ecosystem-type technologies that can support entire business processes. Assisting employees with individual tasks is good, but complex solutions may be even better. Today, AI agents are developed to work across multiple systems, retrieve information, trigger actions, and help move challenging workflows from start to finish. In this respect, agentic AI is treated as one of the next stages of enterprise AI adoption, particularly in highly regulated sectors such as banking.
What makes agentic AI different?
Most AI currently used in banking falls into one of two categories.
Predictive AI analyses historical and real-time data to identify common patterns, estimate associated risk, or detect anomalies. Fraud detection systems and credit scoring models are among the most familiar examples.
Generative AI, in turn, creates some content based on data pool analysis and trained skills in response to user requests. Banks have increasingly deployed generative AI to summarize documents, answer customer questions, assist employees with internal knowledge databases, and draft communications.
Agentic AI introduces a different (third) operating model. Instead of producing a single output, an AI agent can plan a sequence of actions, select appropriate tools, interact with multiple systems, evaluate intermediate results, and continue working toward a defined objective within predefined limits.
Here’s how that works in practice. For example, we have an AI agent supporting a mortgage application. It can, in theory, collect missing documentation, verify customer identity, consult internal lending policies, request additional information when necessary, and prepare the application for final review. This multitude of processes typically takes several commands if run by predictive or generative AI. Now, agentic AI needs a single task that it effectively subdivides into smaller actions. Rather than automating one isolated task, AI agent coordinates a workflow that would traditionally require multiple employees and systems.
This workflow-oriented approach reflects how many banking processes already operate within the system. And that system is often more fragmented than it should be. Customer onboarding, lending, compliance, and fraud investigations often span several departments and technology platforms. As a result, financial institutions are increasingly evaluating AI agents as a way to coordinate existing processes instead of simply automating individual tasks.
In particular, Gartner has argued that much of the long-term value of agentic AI will come from redesigning enterprise workflows rather than adding AI to standalone processes. This course of technology development is not without its disadvantages for some ecosystem participants, though. Namely, Gartner predicts that agentic AI growth will lead to a so-called “Saaspocalypse” – a term George Brockhlehurst, Managing VP at Gartner uses to describe the disaggregation of the legacy SaaS market.
“Agentic AI changes the economics of software,” Brockhlehurst notes. “Agentic systems deliver outcomes directly, bypassing traditional user experience (UX)-heavy applications and making the software invisible. This breaks the link between user growth and revenue growth for many enterprise software vendors.”
Where is agentic AI already being used?
Although agentic AI has attracted significant attention, fully autonomous banking remains uncommon. Most current deployments operate under human supervision. AI is handling repetitive and data-intensive work while employees remain responsible for approvals and high-impact decisions.
There are several reasons for this. First, the banking sphere handles sensitive financial information, and entrusting AI fully with handling that requires great system sophistication and robust controls. The industry is surely moving there, but is not quite out of experimental stage yet.
Second, not only agentic AI authorisation and coordination rails are still in development but also most regulations associated with AI agent use in finance are work-in-progress. People want secure guardrails before they’re ready to embrace more agentic autonomy in banking. That’s why most banks remain reluctant to rush with the AI experiments at scale, preferring to choose automated solutions clients actually want.
“AI is an exciting technology with so much potential, however, there’s no point in launching something if no-one is going to use it.”
Abbey Novack, CashPro Service Product Executive in Global Payments Solutions at Bank of America
Current implementations are emerging across several operational areas.
Customer onboarding is one of the most active use cases, with AI agents collecting documentation, performing identity verification, identifying missing information, and preparing applications for review.
Fraud investigation teams are also beginning to use AI agents to gather evidence from multiple internal systems, summarize findings, and assist analysts during investigations.
In lending operations, AI agents can automate document verification, policy checks, customer communications, and parts of the application workflow before a loan reaches a credit officer.
Compliance and anti-money laundering (AML) teams are currently exploring AI agents that summarize suspicious activity, prepare case documentation, and monitor regulatory requirements.
As for internal back office use, banks are deploying AI agents to help employees retrieve policies, summarize customer histories, coordinate tasks across enterprise applications, and reduce time spent searching for information.
Across these examples, the objective is generally to improve operational efficiency rather than replace human judgement. Final responsibility for lending decisions, regulatory reporting, and financial advice continues to rest with qualified employees.
Why banks are investing
According to a 2025 research by Hawk and Chartis, 82% of banking compliance and risk leaders plan to increase their AI investment by more than 25% over the next two to three years. Banks are evaluating agentic AI for several operational reasons beyond reducing costs.
Completing a single customer request often requires employees to move between core banking platforms, CRM systems, compliance databases, document repositories, payment systems, and communication tools. AI agents aim to automate the coordination of such interactions to minimize redundant manual work and create steady workflows.
Thanks to the continuous operation of software agents, institutions benefit from such solutions because they enable them to monitor processes in operations, detect exceptions, and initiate operations automatically, without human intervention. According to Wolters Kluwer, 44% of finance teams are expected to use agentic AI in 2026, representing a 600% increase year-over-year.
The financial sector is also investigating the potential of AI to speed up time frames for operations that do not require complicated financial analysis but rather information collection from several sources. Reuters has reported that major global banks are expanding the use of AI assistants across wealth management, treasury operations, onboarding, and internal support functions while maintaining human oversight for higher-risk activities.
The adoption gap
Although agentic AI has become a strategic priority for many financial institutions, production deployments remain at an early stage.
Gartner’s 2026 Hype Cycle illustrates the difference between strategic interest and operational implementation. According to the research, only around 17% of organizations have deployed AI agents, while more than 60% expect adoption within the next two years. The figures suggest that investment plans are currently advancing faster than enterprise-wide deployment. There are several technical and governance requirements that continue to slow implementation.
The effectiveness of AI agents is determined by the presence of high-quality information, high-tech APIs, established business procedures, and governing systems that define the right to process data, present audit trails, and implement security protections. Many banks still rely on outdated legacy core systems that hinder end-to-end automation despite the emergence of AI technology.
Implementation projects regularly uncover operational problems that have existed before AI was even introduced, such as fragmentation of core processes, poor data quality or its inconsistency, duplicated workflow, and disconnected legacy infrastructure. Solving such issues is often a requirement for a successful implementation. Regulation might also stand in the way of innovation, being not yet adapted to the new technology context. Here’s just one example of such inconsistency:
“Credit risk AI needs vast data, but GDPR restricts usage. We must find a balance for keeping systems safe while fostering innovation.”
Ivana Jolic, Director at the Croatian National Bank
Regulators are also paying closer attention to governance, explainability, model risk management, and accountability as financial institutions expand the use of increasingly autonomous AI systems. These considerations are expected to shape deployment strategies to a no lesser extent than technological capabilities.
What comes next?
Current deployments generally position AI agents as workflow orchestration and decision-support tools rather than fully autonomous decision-makers. PaySpace Magazine Global believes it will remain this way for a while. Despite the increasing presence of automation, human intervention will remain important when it comes to lending, compliance, investment, and providing financial services to customers.
At the same time, financial institutions are gradually shifting focus from utilizing chatbots to the question whether banking operations can be carried out entirely with the help of automated systems. The speed of AI adoption depends not only on the progress in the development of AI technologies but also on banks’ abilities to improve existing infrastructure, add control mechanisms, and integrate these systems in the daily workflow.
As the world is still far from broad implementation of autonomous banking, AI technologies are gradually making their way from laboratory conditions to some operational applications. The goal of AI technologies nowadays is to simplify operations of banks that are becoming increasingly digital. This symbiosis of automation and process management is why the use of AI in banking is one of the major technology trends in 2026.
Frequently Asked Questions
What is agentic AI in banking?
Agentic AI in banking applies to the systems using artificial intelligence with the ability to plan, organize, and implement multi-step actions to achieve a goal. Unlike the traditional AI, which only provides predictions and responds to queries, agentic AI acts in banking systems, gathers information or takes actions, and adjusts its behavior based on existing rules.
How is agentic AI different from generative AI?
Generative AI produces content, such as text, summaries, and answers to questions. Agentic AI is AI that combines logic with planning and implementation actions. Instead of giving a single answer, it can carry out a chain of actions, like gathering documentation, reviewing internal regulations, or organizing a loan application for assessment.
Is agentic AI already being used by banks?
Yes, though most uses are limited to particular processes in operations. There is no fully autonomous banking at present. It is important to see that financial organizations make use of artificial intelligence agents to carry out complicated tasks, such as supporting clients in joining the bank, managing documentation, conducting investigations regarding cheating, and complying with the standard protocols of work. However, despite the use of AI, it is still important to engage human workers in the process for control and integrity.
Can agentic AI approve loans or make financial decisions on its own?
Overall, it is true that many banks currently do not use AI as their decision-makers. Most banks mainly use their agents to automate the processes of information collection, document verification, and workflow management in various operations. Thus, banks have human professionals responsible for the final decisions in lending, investment advice, regulatory reporting, and other high-risk activities.
What are the main benefits of agentic AI in financial services?
Agentic artificial intelligence saves an enormous amount of time and makes the processes more efficient by integrating several tasks into one workflow.
What challenges are slowing adoption of agentic AI?
The main obstacles include legacy banking systems, dispersed information sources, unadapted compliance rules, IT securitization worries, and adherence to laws and regulations. Many financial organizations still require advanced application programming interfaces (APIs) as well as clearly defined business procedures for the AI agents’ successful and coordinated work across several platforms.
Will agentic AI replace bank employees?
Most experts from the financial services sector suppose that AI agents will be able to enhance the effectiveness of human banking employees, not fully replace them. The current implementations aim to remove repetitive tasks and to allow workers to focus their time on interactions with clients, making difficult decisions, supervising regulatory compliance, and dealing with complicated situations.
