The Role of Agentic AI in Banking
Credit: Roy Marsh, Claydon Consulting Ltd, October 2026
AI, Agentic AI and the Future of Global Payments Systems Development
How intelligent automation is reshaping banking architecture, accelerating innovation and redefining the future of payments.
The banking industry stands at the threshold of its most significant technological transformation since the advent of online banking. While artificial intelligence (AI) is already delivering value through fraud detection, customer service and operational efficiency, the emergence of Agentic AI represents a far more profound shift. For banks, and particularly for organisations operating complex global payments infrastructures, the combination of AI and autonomous agents has the potential to fundamentally change how systems are designed, developed, operated and evolved.
As payments become increasingly real-time, cross-border and data-rich, traditional approaches to systems development are struggling to keep pace with growing demands for speed, resilience, compliance and innovation. Agentic AI may provide the breakthrough that enables banks to meet these challenges while dramatically reducing the cost and complexity of technology delivery.
From AI to Agentic AI
Most banking executives are already familiar with AI applications such as:
Fraud detection
Credit risk assessment
Customer service chatbots
Transaction monitoring
Document processing
These solutions are generally predictive or analytical. They provide recommendations, classifications or insights that humans then act upon.
Agentic AI goes a step further.
An AI agent can be assigned a business objective and autonomously determine how to achieve it. Rather than simply identifying an issue, the agent can analyse the situation, formulate a plan, take actions, monitor results and adapt its behaviour in response to changing conditions.
In effect, Agentic AI moves from answering questions to executing processes.
For banks, this represents a shift from automation to autonomous execution.
Why Payments is the Ideal Environment for Agentic AI
Few banking domains are more suitable for AI-driven transformation than payments.
Global payments ecosystems are characterised by:
Multiple messaging standards
Complex routing rules
Diverse regulatory requirements
High transaction volumes
Tight operational service levels
Constant market evolution
The migration toward ISO 20022, the expansion of real-time payment schemes and increasing expectations for cross-border payment transparency are adding further complexity.
Payment operations teams often spend thousands of hours managing exceptions, investigating failures, resolving liquidity issues and coordinating across internal and external stakeholders.
Agentic AI offers the opportunity to automate many of these knowledge-intensive activities.
Imagine an intelligent payments operations agent that can:
Detect a payment processing bottleneck
Identify the root cause
Assess customer impact
Execute approved remediation actions
Notify relevant stakeholders
Continuously monitor resolution progress
What currently requires multiple teams and several hours could potentially be completed in minutes.
Transforming Systems Development
While operational benefits are significant, the longer-term impact may be even greater within technology delivery and systems development.
Historically, banking projects have followed a familiar pattern:
Business requirements → Design → Development → Testing → Deployment → Support
Despite advances in agile delivery and DevOps, many payments transformation programmes remain expensive, slow and dependent on scarce specialist expertise.
Agentic AI is beginning to disrupt every stage of this lifecycle.
Business Analysis
Requirements gathering has traditionally relied on workshops, interviews and extensive documentation.
AI agents can now:
Analyse regulations and scheme rulebooks
Extract business requirements automatically
Identify process impacts
Generate user stories
Highlight potential implementation risks
For complex payment programmes, this could dramatically reduce analysis timescales while improving completeness and consistency.
Solution Design
Future architecture teams may increasingly work alongside specialist AI agents that can:
Assess multiple architectural patterns
Validate non-functional requirements
Evaluate security implications
Generate integration models
Recommend optimal deployment approaches
Rather than replacing architects, AI will act as a force multiplier, enabling smaller teams to deliver significantly more capability.
Software Development
Generative AI has already demonstrated its ability to produce quality code.
The next evolution involves collaborative AI development teams where specialist agents undertake:
API development
Test script generation
Database design
Interface creation
Security validation
Performance optimisation
Human developers remain responsible for governance, quality assurance and architectural integrity, but routine coding activities become increasingly automated.
Testing and Quality Assurance
Payments systems demand extremely high levels of reliability.
Agentic AI can transform testing by:
Automatically generating test cases
Creating synthetic transaction data
Simulating payment failures
Identifying edge-case scenarios
Performing continuous regression testing
Instead of testing being a bottleneck, it becomes an ongoing autonomous capability integrated into the delivery pipeline.
The Future Global Payments Platform
The most exciting developments lie beyond development productivity and into the operational architecture of future payments platforms themselves.
Tomorrow's global payments systems may consist of multiple intelligent agents working collaboratively across the payment lifecycle.
Intelligent Routing Agents
These agents could dynamically determine the optimal route for a payment based on:
Cost
Speed
Liquidity availability
Regulatory constraints
Customer preferences
Routing decisions would be made in real time, continuously adapting to changing market conditions.
Liquidity Management Agents
Banks currently dedicate significant resources to optimising liquidity across payment schemes and correspondent networks.
AI agents could:
Forecast liquidity demands
Rebalance positions automatically
Minimise funding costs
Anticipate settlement risks
The result would be more efficient capital utilisation and improved payment performance.
Compliance Agents
As regulatory expectations continue to increase, compliance functions face mounting operational pressure.
Agentic AI could continuously monitor:
Sanctions screening
AML controls
Transaction reporting
Scheme compliance
Regulatory changes
Rather than periodic reviews, compliance becomes a continuously operating capability.
Incident Management Agents
Production support teams could see some of the most immediate benefits.
An incident management agent might:
Detect anomalies
Correlate related events
Identify root causes
Implement approved fixes
Produce post-incident reports
This has the potential to dramatically improve service availability and reduce operational risk.
Challenges and Governance Considerations
While the opportunities are compelling, banks must proceed carefully.
Financial services remains one of the most heavily regulated industries in the world.
Key challenges include:
Explainability
Regulators will require banks to understand and explain AI-driven decisions, particularly where customer outcomes are affected.
Model Risk Management
Agentic systems require rigorous monitoring, validation and governance frameworks to ensure predictable and controlled behaviour.
Security
Autonomous agents must operate within clearly defined boundaries, particularly when interacting with sensitive payment infrastructure.
Accountability
Ultimately, responsibility for decisions remains with the bank, regardless of the level of automation involved.
Successful organisations will therefore adopt a "human-supervised autonomy" model, where agents act independently within carefully defined parameters.
What Banking Leaders Should Do Now
Waiting for the technology to mature fully is unlikely to be a successful strategy.
The organisations gaining competitive advantage today are already experimenting with AI-enabled delivery and operations.
Banking leaders should focus on:
Identifying high-volume operational processes suitable for AI augmentation.
Establishing governance frameworks for AI adoption.
Building internal AI literacy across business and technology teams.
Exploring AI-assisted software engineering practices.
Developing a clear roadmap towards agent-enabled payment operations.
The objective is not simply cost reduction. It is creating organisations capable of adapting at the speed increasingly demanded by customers, regulators and markets.
Conclusion
The future of banking technology will not be defined by whether AI is adopted, but by how effectively institutions integrate intelligent agents into their operational and development ecosystems.
For global payments organisations, the implications are particularly significant. Agentic AI has the potential to transform everything from requirements gathering and software development to liquidity management, compliance and operational resilience.
Just as cloud computing reshaped banking infrastructure over the last decade, AI and Agentic AI are poised to reshape how payments systems are conceived, built and operated in the decade ahead.
The banks that succeed will not simply use AI as a tool. They will redesign their technology and operating models around intelligent, collaborative agents capable of delivering faster innovation, greater resilience and a fundamentally different approach to payments processing.
At Claydon Consulting, we help financial institutions navigate complex technology transformation programmes, payment modernisation initiatives and emerging technologies. As AI and Agentic AI move from experimentation to enterprise adoption, the challenge is no longer whether to embrace these technologies, but how to do so safely, strategically and at scale.
Comments