Keyword: «credit risk»

At the present time the economic situation in the country, and across the country as a whole, is very diffi-cult. All regions of the Russian Federation it is possible to combine a common problem – lack of funding, and as a result, the region is unattractive from an economic point of view, namely unattractive for private investors.

The article examines the economic nature of credit risk in the banking sector and presents its main characteristics. The authors examine the credit risk analysis of the banking sector organizations at the current stage, provide information on credit organizations with bad debts in the loan portfolio, assess the financial coefficients of the loan portfolio quality and identify the main areas of loan portfolio quality management in modern conditions to maintain the stability of credit organizations and secure a profit.

This article discusses the essence of the concept of "creditworthiness", identified the main ways to assess the creditworthiness of the borrower. In addition, the calculation of the solvency index by the method of PJSC "Sberbank" is presented.

Big Data is used everywhere today and the activities of financial institutions are no exception. The advantages of big data analytics technologies lie in the volume and speed of processing various types of information, which contributes to an increase in the level of automation, ensures the optimization of business processes and contributes to the improvement of financial markets. In addition, big data enables financial institutions to provide more personalized services to consumers and expand their customer base.