Literature Review on Optimizing Cash Flow Forecasting Using Machine Learning in Small and Medium Enterprises

This research aims to assess how the application of machine learning (ML) can enhance the accuracy and efficiency of cash flow forecasting in small and medium enterprises (SMEs) and to identify the relationship between ML adoption and improved financial decision-making within these enterprises. The methodology used is a systematic literature review following the PRISMA framework. The analysis is based on selected papers that meet specific criteria and address the research questions of this study. The findings reveal that various ML models can enhance cash flow forecasting by utilizing financial data and capturing underlying patterns. The study concludes that ML is beneficial for SMEs, helping to reduce insolvency rates through improved cash flow forecasting and enabling better financial decision-making based on wellmanaged financial data.