Authors: Krishnaben Kachiya
Abstract: Small and medium-sized enterprises (SMEs) operate in increasingly uncertain business environments characterised by inflation, rising interest rates, supply chain disruptions, geopolitical instability, and changing customer behaviour. These conditions have exposed significant limitations in traditional cash flow forecasting methods, which rely primarily on historical accounting information and static budgeting techniques. This study investigates the role of predictive cash flow modelling in strengthening SME financial resilience through the application of predictive analytics, artificial intelligence (AI), machine learning, and digital financial technologies. A qualitative secondary research methodology was adopted using systematic thematic analysis of peer-reviewed journal articles, professional reports, and institutional publications. The findings demonstrate that predictive cash flow modelling improves forecasting accuracy, enhances liquidity management, supports working capital optimisation, and enables proactive financial decision-making. The study develops a conceptual framework integrating the Resource-Based View, Dynamic Capabilities Theory, Contingency Theory, and Enterprise Risk Management to explain how predictive financial capabilities contribute to sustainable organisational performance. The research concludes that predictive cash flow modelling represents a strategic organisational capability rather than merely a technological forecasting tool, and that investment in digital financial infrastructure, employee analytical capability, and integrated financial management systems is essential for improving SME resilience within volatile global markets.
