Over the past few months I conducted primary and secondary research to understand:  The Impact of AI in Treasury and Payments.

Artificial Intelligence is rapidly becoming embedded in treasury and payments. But while adoption is accelerating, value realisation remains uneven.

According to the Bank of England and FCA (2024):

• 75% of financial services firms already use AI
• 46% report only partial understanding of the technologies they deploy
• 55% of use cases involve automated decision-making

At the same time, research shows that AI can:

• improve cashflow forecasting accuracy by up to 30%
• reduce manual effort across reconciliation, reporting, and payments
• enhance risk analysis and scenario modelling

Yet many organisations are not seeing these benefits at scale.

The Core Issue

The challenge is not the absence of AI tools.

It is the weakness of the foundations beneath them:

• fragmented and inconsistent data
• lack of standardisation across systems and payment rails
• limited governance and control frameworks
• insufficient explainability and accountability

As a result, AI is often deployed in isolation — without the structure needed to support it.

What This Paper Covers

This white paper examines:

• where AI is already creating value in treasury and payments
• why adoption is outpacing control and understanding
• the structural barriers limiting impact
• the emerging risks around automation, third-party dependency, and agentic systems
• how organisations can move from fragmented experimentation to controlled, scalable value

Key Conclusion

The success of AI in treasury and payments will not be determined by model sophistication.

It will depend on:

• strong data foundations
• clear governance and accountability
• disciplined operating models
• alignment between AI and decision-making

Access the full white paper:

👉 Artificial Intelligence in Treasury and Payments