APPLICATION OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING ALGORITHMS IN DETERMINING A PERSON'S SOLVENCY

This study provides information on decision-making in determining the solvency of an individual, which has become a topical issue today. It is devoted to research on the application of artificial intelligence and machine learning algorithms in determining the solvency of individuals. The study analyzes intelligent systems and technologies used by financial institutions, credit bureaus, and insurance companies to automate decision-making processes, increase accuracy, and minimize financial risks. The study discusses the characteristics, advantages, and limitations of credit scoring systems such as FICO Score and VantageScore, banks' internal models, as well as modern machine learning algorithms such as Gradient Boosting (XGBoost, LightGBM, CatBoost), Random Forest, Logistic Regression, Support Vector Machines, and neural networks. A combined approach (traditional scoring systems and artificial intelligence, machine learning algorithms) is highlighted as the most effective strategy for ensuring financial stability and optimizing decision-making processes. In the future, the integration of innovative technologies such as artificial intelligence, machine learning, and blockchain is expected to provide more sustainable and effective solutions for the financial sector.