Objective. The purpose of this article is to provide a theoretical justification and practical analysis of the possibilities of using artificial intelligence tools to modernise the methods of assessing the creditworthiness of enterprises. The article also examines the impact of these technologies on the formation of effective financial strategies in the activities of financial and credit institutions. Methodology. The methodological basis of the study includes a set of general scientific and special methods that provide a comprehensive analysis of the use of artificial intelligence to assess the creditworthiness of enterprises. The use of these methods allows us to scientifically substantiate the results obtained and develop practical recommendations for financial institutions to optimise credit analysis. Results. The study has shown that the use of artificial intelligence significantly increases the likelihood of an accurate assessment of the creditworthiness of enterprises and eliminates the impact of human error. Traditional methods of credit analysis have significant limitations, while artificial intelligence enables fast processing of large amounts of data and automated decision-making. The most promising technologies in this area are machine learning, neural networks, and Big Data, which help improve financial risk assessment. At the same time, the report highlights challenges, including the high cost of implementation, the need for specialist training, and cybersecurity risks. An analysis of the regulatory framework has shown that legislation needs to be adapted to digital technologies, especially in the area of liability for automated solutions. Artificial intelligence is already being actively used in the banking, insurance and financial technology sectors, helping to reduce costs and improve risk management. The use of artificial intelligence can significantly reduce the number of defaults and allow for the creation of more personalised financial products. Thus, the use of artificial intelligence opens up new horizons for financial strategies that meet modern economic challenges. Scientific novelty. This study is a comprehensive analysis of the possibilities and implications of using artificial intelligence in assessing the creditworthiness of enterprises, taking into account current trends in the digitalisation of the financial sector. The main challenges and benefits of introducing artificial intelligence in financial institutions are systematised, and a comparative analysis of traditional and modern methods of credit analysis is carried out. The study expands the scientific understanding of the application of machine learning algorithms and neural networks in credit scoring, which allows for the improvement of financial strategies and risk management. Practical significance. The study can be useful for financial institutions in improving credit assessment and risk management methods. The findings will contribute to the development of effective machine learning algorithms that will improve the accuracy of credit analysis and automate decision-making. The use of artificial intelligence helps to optimise financial processes, reduce credit risks and improve the quality of banking and insurance services.
DOBRYK et al. (Sat,) studied this question.