Correlation-regression analysis of lending services of commercial banks (in the example of second-tier banks of the Republic of Kazakhstan)

The purpose of the article is to study the interdependence of factors and analyze their priority by identifying several factors in the lending activities of second-tier banks of the RK. To achieve this goal, the following tasks were set: familiarization with the scientific works of domestic, near, distant foreign scientists, selection of 12 (X) different factor indicators influencing the main (Y) factor, determination of the density of connections between the selected factors and construction of a correlation-regression matrix. The study of correlation and regression analysis of the lending activities of commercial banks is of increasing interest to world scientists. In the Message for 2023 and 2024 to the people of the Republic of Kazakhstan, President of the Republic of Kazakhstan K. Tokayev mentioned ways to eliminate the imbalance between monetary and fiscal policies, finance medium and small businesses, as well as increase the influence of banks on economic growth. In correlation and regression analysis using the three-factor model obtained from the study, the determination of the density of factors affecting the credit market at different levels reveals the relevance of the article because as the selection of the main indicators obtained from the correlation and regression equation allows for proper decision making in the activities of second tier banks. In order to achieve the results of building a dynamic model in the course of the research article selected statistical data RK 2001-2024, in the correlation analysis were analysed 12 factors of influence t-statistical computer program and correlation matrix, created a correlation matrix in order to analyse the close relationship between the given indicators. Summarising the research work, it is possible to note the simultaneous close relationship between the characteristics of the result and factor characteristics according to the analysis, on the basis of the obtained regression equation determined statistical significance and reliability in general and regression coefficients at the appropriate levels of significance.