AI agents for cash management in payment systems
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- Assistant-level AI agents can reproduce basic cash-management heuristics, including precautionary liquidity management, payment prioritization, and liquidity–delay trade-off in stylized RTGS scenarios.
- The agent’s responses are broadly consistent in simple settings, but consistency weakens as scenarios become more complex, highlighting the need for benchmarks and further assessment.
- The results point to a promising role for AI in payment operations, but realistic use may require purpose-built tuning, rigorous benchmarks, and clearly defined governance controls.
Using prompt-based experiments with ChatGPT’s reasoning model, we evaluate whether generative artificial intelligence models can assist with high-level intraday liquidity-management tasks in wholesale payment systems. We simulate stylized payment scenarios with liquidity shocks and competing priorities to test the agent’s ability to maintain precautionary liquidity buffers, prioritize payments under tight constraints and reason through the trade-off between settlement speed and liquidity usage. Our results show that even without domain-specific training, the artificial intelligence agent produces recommendations that are broadly consistent with key cash-management practices to preserve liquidity while minimizing delays. These findings suggest that general-purpose artificial intelligence models may assist routine cash-management decisions and help lay the groundwork for specialized systems that could automate bounded tasks, potentially reducing operational costs and improving intraday liquidity efficiency. We conclude by outlining the key risks, relevant policy and regulatory safeguards, and future research directions for artificial-intelligence-driven payment operations.
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