AI-Powered Forecasting Models for Optimizing Working Capital in Supply Chain Financing
Shiv Kumar Agarwal, Jhalak Agrawal
Abstract
Modern supply chain finance has the critical issue of efficiently managing its working capital based on uncertain cycles of demand, prompt payment, and credit gaps between supplier and buyer. The use of AI-based forecasting models is a sophisticated way of forecasting about the cash flow trends, inventory requirement, and credit risk evidence-based financial decisions, proactive, and dynamic financial decision-making are possible. The proposed project creates a comprehensive AI system, which is composed of deep-learning, time-series forecasting, and reinforcement learning and which allows to make the working capital more streamlined across the supply chains. The model uses real-time processing of financial transactions, payment history, supplier behaviour and macroeconomic indicators, to give accurate predictions on liquidity needs and allocation of credit. It is also easy to adjust to seasonal changes and changes in the market with this system and businesses can maintain payment terms, inventory costs and cash balances better than with a manual system. The result of the multi-industry simulation reveals that the proposed model can provide a significant increase of capital utilization, decrease in financing costs and a decrease in the liquidity risk. In contrast to the classical rule-based method, the AI-powered strategy makes it possible to constantly optimize the position of working capital with fluctuations of business processes. The results point to the transformational potential of artificial intelligence on hardening up the supply chain financing in the present-day complex economic environment where forecast and react occurs to be more responsive and data-centric. The efficiency of the proposed system to reduce its cost using other approaches is 89.1% and abnormality response rate of 93%.