Corporate treasury sits at the intersection of every financial decision and every risk a company carries. The tools have improved over the years. The decisions have stayed human. Agentic artificial intelligence is beginning to change that.

AI agents are now performing core treasury functions autonomously, from intraday liquidity decisions in wholesale payment systems to FX exposure forecasting and cash flow optimization. The shift is happening inside central bank research, inside corporate treasury teams across EMEA, and inside the largest banks in the world.

For example, a BIS Working Paper by Iñaki Aldasoro and Ajit Desai of the Bank for International Settlements and the Bank of Canada tested whether a generative AI agent could perform intraday liquidity management inside a wholesale payment system without domain-specific training, using ChatGPT’s o3 reasoning model across a series of simulated cash manager scenarios.

The paper said the agent closely replicated key prudential cash management practices. When facing two small pending payments and the possibility of a large urgent payment shortly after, the agent chose to delay the smaller payments to preserve liquidity, a strategy consistent with how experienced human cash managers operate. When complexity increased, with probabilistic inflows and competing payment priorities, the agent adapted its reasoning, though consistency dropped slightly as trade-offs became more layered.

The paper tested the agent in an operator mode, running it through a structured set of stylized cash management scenarios. The agent completed the exercise autonomously, responding correctly across routine liquidity prioritization scenarios while deferring to human oversight when it encountered potentially anomalous payment patterns.

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