A computer model for analyzing, evaluating, and forecasting bank credit risks has been developed. Utilizing a Bayesian network (BN) and established parameter estimation methods, this model was implemented in the Python programming language. It predicts the probability that a borrower may fail to meet financial obligations, such as repaying a loan.
Due to large-scale changes in the economy in the world and in Ukraine in particular, there has recently been a significant increase in interest in the problems of investment theory. An example is the intensification in recent years of the purchase of shares of large international companies and cryptocurrencies and, according to the rapid growth of their values. It is known that as a special case, the theory of investment considers the task of optimizing investment portfolios.
The article raises questions of improvement of research methods legal and administrative events, identify the place and role of structural-functional and factor analysis,possibilities of application of methods of applied mathematics. The features of information and statistical tools and methods of system analysis to identify trends in social phenomena that are governed by administrative law.