The problems of investment activity of entrepreneurship and methodological aspects of credit risks assessment

Abstract

The purpose of the scientific article is to identify the features of entrepreneurial activity in the conditions of economic instability and assessment of credit risks of the borrower in order to attract investment resources. The object of research is the entrepreneurial activity. The subject of research is the analysis and methods of credit risk minimization. The following tasks of the research are set to achieve the goal: 1) to identify the features, problems and trends in the development of entrepreneurship in Russia; 2) systematization of factors affecting the investment activity of small business; 3) definition of indicators for the analysis of credit risk of the borrower; 4) development of recommendations for credit risk management in a commercial bank in terms of providing investment resources in modern conditions. The conceptual positions revealing the essence of entrepreneurship and its characteristic features in the market conditions are analyzed. The authors prove the necessity of financial stimulation of entrepreneurial activity in the sphere of formation of investment resources at the expense of credit. The causes of critical risk in the conditions of market volatility and socio-political instability are determined. The process of credit risk management at the stage of credit issuance is considered. The possibilities of using different models of risk assessment in order to make a decision on the issuance of credit resources of investment nature for small business.

Money Supply As An Indicator Of Monetary Regulation

“…The М0 aggregate represents all cash and maximum liquid assets that can be used as a means of circulation and payment. This indicator is usually chosen as the object of monetary regulation, when the prevailing share in payment relations is cash, and the economy is in a "liquid trap" and does not respond to changes in interest rates (Khokhlova, Kretova, & Burov, 2019;Muftahova, Nechaev, & Antipina, 2015).…”

A new model to improve decision-making indicators of bank customers' based on neural networks and particle swarm optimization algorithm

“…= TNN TNN + FPN (9) Where FPN or False-positive numbers that is the number of bad customers that the model has identi ed as good customers.…”

“…This is the main role of banks in the nancial market by providing credit to customers. One of the important issues in this eld is studying and evaluating credit risks [9]. Credit risk refers to the possibility of failure to repay facilities and loans granted by customers.…”