Artificial Intelligence and Working Capital Management: Implications for Financial Efficiency and Financial Decision-Making in Nepal

Abstract

This study examines the role of artificial intelligence (AI) in working capital management (WCM) and investigates how effective WCM contributes to financial efficiency and financial decision-making in modern businesses. The study adopts a quantitative research design and focuses on finance and business professionals involved in financial decision-making. Data were structured around AI-based financial forecasting, AI-based decision support, working capital management, financial efficiency, and financial decision-making. The proposed relationships were examined using Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS 4. The results indicate that AI-based financial forecasting has a significant positive effect on working capital management (β = 0.354, p < 0.001), while AI-based decision support also has a significant positive effect on WCM (β = 0.208, p < 0.001). Working capital management significantly improves financial efficiency (β = 0.533, p < 0.001) and financial decision-making (β = 0.389, p < 0.001). The model explains 21.6% of the variance in WCM, 28.4% in financial efficiency, and 15.2% in financial decision-making. The findings suggest that integrating AI-based forecasting and decision-support capabilities with working capital practices can strengthen financial planning, improve the management of short-term financial resources, and support more informed financial decisions. The study connects AI-enabled financial capabilities with working capital management and its downstream effects on financial efficiency and decision-making, providing an integrated framework for understanding the role of AI in contemporary financial management.