Reinforcement Learning for Liquidity Optimization of Multinational Enterprises Overseas Treasury Cash Pools in the Digital Economy

This paper develops a reinforcement learning framework for optimizing liquidity allocation in multinational enterprises' overseas cash pools. The framework models a multi-regional digital-treasury environment incorporating currency conversion, regulatory frictions, time-zone misalignment, cut-off times, intercompany limits, and intraday payment volatility. An actor-critic agent observes multi-currency balances, forecasted cash flows, FX quotes, and regulatory flags to recommend sweep amounts, buffer adjustments, and internal loans at discrete intraday intervals. The reward function jointly penalizes funding cost, idle balances, constraint breaches, and FX slippage, while safety masks enforce hard limits. Synthetic treasury-plausible data are calibrated to historical distributions of payroll cycles, tax dates, and market stress episodes. Numerical experiments across 9.6×10⁵ simulated days demonstrate that the RL policy reduces net funding cost by 18.7±3.4% and idle liquidity by 26.2±5.1% relative to rule-based approaches, while maintaining regulatory breaches below 0.08±0.02 events per 1,000 transactions. Stress tests confirm graceful performance degradation under FX gaps and sudden outflow shocks. Results suggest enterprise-tailored RL can enhance overseas treasury performance while remaining compatible with operational and compliance requirements.