Empirical Study on the Effectiveness of Generative AI in Financial Risk Management for Small and Medium Enterprises

Abstract

With the rapid development of the digital economy, small and medium enterprises (SMEs) face increasingly complex and diverse financial risks. Traditional financial risk management methods exhibit significant limitations in processing massive data and predicting complex risk patterns. The emergence of generative artificial intelligence technology provides innovative solutions for financial risk management in SMEs. This study focuses on the application effectiveness of generative AI technology in SME financial risk management. Through constructing a theoretical analytical framework and employing a combined research methodology of questionnaire surveys, case analysis, and empirical testing, we conducted an in-depth investigation of 235 SMEs. The research findings reveal that generative AI technology significantly outperforms traditional methods in financial risk identification accuracy, prediction precision, and management efficiency, with risk identification accuracy improving by 27.3% and risk prediction precision increasing by 34.5%. Meanwhile, factors such as technology acceptance, data quality, and organizational support have significant impacts on the effectiveness of generative AI. The research results provide theoretical guidance and practical reference for SMEs to rationally utilize generative AI technology to enhance their financial risk management capabilities.