This study aims to explore how Artificial Intelligence (AI) technologies, notably machine learning and data analysis, can be utilized to optimize the financial management processes of Small and Medium-sized Enterprises (SMEs). With the continuous advancement and proliferation of AI technologies, their potential application in the realm of financial management has become increasingly evident, especially for SMEs with relatively limited resources. Through the analysis of existing literature and case studies, this research investigates the use of intelligent algorithms for the analysis and prediction of financial data, intending to enhance the accuracy and efficiency of financial decision-making. The research methodology combines qualitative and quantitative approaches, collecting financial data from SMEs of various sizes and industries to apply machine learning algorithms. The primary technologies used include linear regression, decision trees, and neural networks, aimed at trend prediction, risk assessment, and decision optimization of financial data. Key findings indicate that the use of machine learning and data analysis technologies can significantly optimize the financial management processes of SMEs. Specifically, machine learning algorithms excel in the accuracy of financial data predictions, effectively assisting in revenue forecasting and cost control. Additionally, data analysis aids in identifying financial risks and opportunities, thereby providing data-driven decision support for businesses. Practical applications also demonstrate that financial management process optimized by AI technology improves operational efficiency and financial transparency of enterprises. The conclusion emphasizes that the optimization of financial management through AI not only enhances the decision-making precision and efficiency for SMEs but also fosters innovation in financial management.
Yu Miao (2024) studied this question.