The Role of Artificial Intelligence in Strengthening Financial Practices of SMEs
The Role of Artificial Intelligence in Strengthening Financial Practices of SMEs

Summary/Abstract: Research background: Artificial intelligence (AI) is rapidly transforming financial management in SMEs. Advances in machine learning, natural language processing and generative AI enhance forecasting, credit assessment, risk management and fraud detection. Despite this potential, SMEs face limited data, resource constraints and skill gaps, which significantly influence the pace and form of AI adoption. Purpose of the article: The article aims to review high-quality research published between 2016 and 2024 on AI applications in SME finance. It focuses on how AI is used in financial forecasting, credit scoring, risk management, fraud detection, and financial planning, and examines how it affects SME financial stability, performance, and growth. Methods: The study draws on a focused review of high-quality research (2016-2024) on AI in SME finance, synthesizing empirical and conceptual studies on AI applications, their documented benefits, key challenges, and strategies that enable effective financial management adoption. Findings & Value added: The review shows that AI offers strong potential to enhance the financial stability and growth of SMEs when supported by suitable organizational capacities and governance. Key benefits include improved cash flow and financial forecasting, more accurate credit risk assessment, real-time fraud detection, and more data-driven financial planning. Despite these advantages, SMEs face barriers such as limited data, skill shortages, and high implementation costs. The review identifies strategies to overcome these challenges, including cloud-based AI solutions, targeted employee training, and explainable AI to strengthen transparency and trust. Ethical concerns, especially algorithmic bias and the need for human oversight remain essential for ensuring positive financial outcomes. The article adds value by synthesizing fragmented evidence, linking AI use to performance improvements, and outlining directions for future research on sustainable AI adoption.

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