Autonomous Treasury Operations: Evaluating AI-Driven Dynamic Yield Optimization and Cash Management in Commercial Enterprises
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This study helps to assess the potential to disrupt the conventional methods of corporate cash management through the incorporation of the use of predictive artificial intelligence (AI), automated liquidity management, and machine learning yield optimization into the corporate treasury management system. The existing practice of treasury management is still limited to conventional methods of working capital forecasts, conventional allocations of commercial bank deposits, and treasury management evaluations done periodically with the effect of creating cash drag, inefficient usage of capital, and loss of interest yield. This study will confirm if an AI-based dynamic liquidity management system can improve portfolio interest yields, forecast precision, and capital management in light of these constraints. Two treasury management systems were used concurrently for three months during the case study of a medium-sized commercial company: the conventional treasury management system and an innovative technology-based system using AI algorithms to allocate daily surplus cash balances among money market funds, treasury bills, and high interest-bearing commercial deposits. The five indicators of performance are the annualized net treasury yield, the daily cash forecasting error rate, the daily treasury operation management hours, the idle cash drag percentage, and the transaction execution cost which were determined for approximately 10,000 corporate transaction allocations. It is seen that there has been a significant improvement in the performance of the treasury management environment in the AI age. There has been an increase of 38 percent in the portfolio yield return, there has been a decrease of 52 percent in daily treasury operation execution time, time taken in weekly liquidity planning has been significantly reduced, there has been a reduction of 74 percent in cash forecasting error rates and there has been a reduction of 34 percent in transaction execution costs. The implications of these results are: with the help of cash flow monitoring in real-time, liquidity optimization through prediction and cash sweeping through automation, there can be a positive impact on corporate financial management with the use of AI and dynamic algorithms.
Keywords
Artificial Intelligence, Automated Cash Sweeping, Cash Management, Dynamic Yield Optimization, Liquidity Management, Machine Learning, Treasury Management, Working Capital Management