Why does reproducibility matter for treasury AI?

A fully agentic process can quietly adapt when underlying data changes, producing two different answers to the same question in successive months. The fix is to explore agentically while a human approves, then freeze the approved workflow and execute it deterministically.

The Risks of AI in Treasury and How to Mitigate Them

The main risks are data security, unexplainable outputs, model drift, and unscoped autonomy. Surveys put data security as treasury's top AI concern, with many teams also citing a lack of internal AI experience. Mitigate with full audit trails, scoped permissions, and human approval on anything that moves money.

Data security

Treasury data is among the most sensitive in the business. Ask where data is processed, how it is encrypted, and whether your data trains models used by other customers. It should not. See how Palm handles this on the security page.

Explainability

If a model cannot show which transactions drove a number, you cannot defend it to audit. The test is simple: can you explain this recommendation six months from now, with a trace back to source data?

Model drift

Models degrade as the business changes. Continuous re-scoring against actuals is not optional. Ask what happens to accuracy after an acquisition or a new entity.

Non-reproducible output

This is the risk most buyers miss. A fully agentic process can quietly adapt when underlying data changes, producing two different answers to the same question in successive months with no explanation.

The fix is a build-time and run-time split: explore agentically while a human approves, then freeze the approved workflow and execute it deterministically. Reproducibility, accuracy, traceability, and explainability are foundational, not features.

Arithmetic

Language models synthesise. They do not calculate reliably. A treasury agent must execute code for arithmetic and ground every figure in your records, never in training data.

Autonomy creep

An agent with broad permissions moves real money fast. Approval thresholds and scoped permissions belong in the design, not on the roadmap. Agentic AI in treasury explains where agents should and should not act alone.

AI-enabled fraud

A growing share of large companies report receiving AI-generated fraud or deepfake payment requests, while far fewer use AI to detect them. The threat is adopting faster than the defence.

For more, read do you need AI agents for treasury? and your TMS has AI on its website, not in the product.

What are the risks of using AI in treasury?