Ripple Treasury expands GSmart AI for finance teams

Ripple Treasury has expanded its GSmart artificial intelligence platform for corporate finance teams, adding functions across several treasury workflows.

The update extends GSmart into forecasting, liquidity, risk, reconciliation and reporting. The system is already in use across Ripple Treasury's enterprise customer base and is built into the policies, data and workflows treasury teams use in day-to-day operations.

At the centre of the expansion is an effort to address a governance problem finance leaders face as artificial intelligence tools spread through large organisations. Ripple Treasury cited Gartner research projecting that the average Fortune 500 company could have more than 150,000 agents in use by 2028, while only 13% of organisations believe they currently have the right governance in place for AI agents.

For treasury teams, the issue is particularly acute because they oversee cash, liquidity and financial risk, where decisions must be documented and checked. GSmart separates financial calculation from AI interpretation, using deterministic engines for calculations while AI interprets policy, identifies patterns and explains recommendations.

Under that structure, proposed actions are presented for human approval before execution. Each decision remains auditable and subject to financial controls.

Renaat Ver Eecke, Senior Vice President of Ripple Treasury, outlined the company's view of the pressure facing finance leaders.

"Every CFO is under pressure to embrace AI, but they're equally responsible for ensuring every financial decision is explainable, governed and compliant," said Ver Eecke.

He said the product was designed to operate within internal policy frameworks rather than replace them.

"Rather than asking customers to blindly trust an AI system, GSmart works within each organisation's own treasury policies to surface recommendations transparently, while ensuring humans remain in control of every decision. This isn't simply AI-native treasury, but rather treasury-native AI," said Ver Eecke.

New functions

The expanded system includes what Ripple Treasury describes as orchestrated agents across forecasting and planning, liquidity, risk, reconciliation and reporting. Each agent monitors a process, proposes a specific action, cites the relevant policy clause and then waits for approval.

Another part of the system is Knowledge Studio, which acts as the policy and governance layer for GSmart. Treasury teams can use it to define organisational policies and controls governing how the AI functions operate, and proposed actions are checked against those controls before being passed to a person for review.

Ripple Treasury has also added Analytics Studio, which includes a conversational tool called Ask GSmart. The feature is intended to help treasury teams retrieve answers and reporting insights from treasury data through a single analytics layer.

Customer uptake

Ripple Treasury also disclosed early usage figures for parts of the system among eligible customers. It said 60% of eligible customers have enabled Risk Insights, which is designed to identify exposure anomalies and policy breaches.

In addition, 44% of eligible customers are using Forecast Insights, a feature that compares forecast and actual cash flows to identify possible liquidity gaps. The figures offer a snapshot of adoption as finance teams test AI tools in areas where governance and explainability remain central concerns.

The expansion is part of Ripple Treasury's broader push to build a wider treasury management platform spanning traditional and digital assets. GSmart adds an intelligence layer across the treasury lifecycle following the introduction of native digital asset functions earlier this year.

Ripple, founded in 2012, provides blockchain-based products for payments, custody, prime brokerage and treasury management. It holds more than 85 licences and uses its stablecoin RLUSD and the digital asset XRP in its financial products.

The latest update positions treasury software as another front in the race to apply artificial intelligence to business operations without surrendering control over regulated financial processes. For corporate finance teams, the challenge remains how to use automation in decision support while keeping approval authority with people and maintaining a clear audit trail for every action.