Agentic AI is fast emerging as a transformative force within financial services, bringing the promise of automated decision-making, hyper-personalisation and operational efficiency.
Agentic AI in banking
But as UK bank Griffin’s pioneering Model Context Protocol (MCP) shows, the path to full-scale deployment will require a reimagining of the financial system itself.
Agentic AI
At its core, agentic AI refers to systems that can independently make decisions and carry out goal-directed tasks based on real-time data and environmental feedback.
By leveraging machine learning, reinforcement learning and multi-agent frameworks, financial institutions are beginning to deploy AI agents across a range of functions — from automating loan approvals to streamlining customer service.
The benefits are substantial.
Agentic AI systems can analyse vast data sets in real-time, tailor financial advice to individual behaviours and even predict user needs before they arise.
Reactive and model-based agents are already used for fraud detection and underwriting, while complex multi-agent systems (CMAS) are transforming areas such as wealth management and portfolio optimisation.
Great Power…Great Risk
But with such power comes significant risk.
Chief among the challenges are regulatory compliance, explainability and data privacy.
In a sector where transparency and accountability are paramount, agentic AI’s black-box decisioning can be difficult to justify to regulators and customers alike.
If a customer is denied a loan by an AI agent, for instance, the institution must be able to explain — in plain terms — why the decision was made.
Security concerns are equally pressing.
As agentic systems gain autonomy, the attack surface for cyber threats expands.
A compromised AI agent making unauthorised transactions poses existential risks to a financial institution’s reputation and customer trust.
Similarly, bias in training data can result in unethical or even unlawful outcomes if not properly mitigated.
Ethics and Liability
Griffin’s introduction of the MCP server signals a bold new direction.
The bank’s fully licensed infrastructure now allows AI agents to autonomously open accounts, execute payments and analyse financial events — all via its sandboxed environment.
The initiative highlights how next-generation banking platforms are beginning to accommodate autonomous software agents as active participants in financial workflows.
While still in beta, the MCP framework offers developers the chance to prototype fintech applications that embed autonomous AI from the outset.
Griffin sees this as the beginning of a broader platform shift where AI agents undertake increasingly complex tasks — not just buying coffee, but managing wealth portfolios, administering payments and navigating regulatory obligations.
Yet, as Griffin notes, the financial system must be “fundamentally rewired” to support this evolution.
Beyond infrastructure, institutions will need new frameworks for governance, ethics and liability.
The UK government and regulators (amongst others), for their part, must balance innovation with oversight to avoid unintended consequences as autonomous systems scale.
The realisation of agentic banking depends not only on technological innovation, but also on solving the thorny challenges of trust, safety and compliance.
If successful, financial institutions may soon find themselves staffed not only by relationship managers and analysts — but by autonomous agents working at machine speed to serve human needs.
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