The monograph contains new scientifically-based results that were obtained when solving the purpose of the work: development and research of alternative approaches and methods of risk management as well as tools based on them for analyzing the credit risk of a commercial bank in consumer lending markets.

Lending itself refers to the profitable activities of banks of any level, but at the same time, the issuance of loans is one of the risky banking operations. The effectiveness of banking activities depends on the degree of optimization of credit risk management, since the success of almost any decision in the field of strategic and tactical financial management is determined by the ability to identify and evaluate risks, carry out credit controlling of the behavioral characteristics of open risk positions that ensure the achievement of the bank's target functions.

The methods used today for assessing the creditworthiness of a borrower should take into account only the current state of the organization and be able to predict the level of financial stability for the entire lending period.

Based on the theoretical and experimental studies performed in the monograph, the author obtained the following main results:

- an analysis is made of existing methods for determining the level of credit risk, the advantages and disadvantages of existing methods for determining the level of credit risk are presented. The use of intelligent systems (neural networks and the theory of fuzzy sets) for risk assessment has been investigated;

- the importance of a qualitative assessment of credit risk is justified;

- identified the most important criteria for determining the credit rating of the borrower. These indicators were divided into four categories: credit history, financial condition, demographic position of the borrower, as well as real estate ownership;

- an architectural model has been developed for assessing credit risk based on fuzzy logic, which can improve its quality and reduce the time it takes to make a decision on a loan application. The architecture of the model consists of the following components: database; fuzzification; means of fuzzy output; base of fuzzy production rules; defazzification.

This model of determining the creditworthiness of borrowers based on the Mamdani fuzzy inference algorithm allows you to take into account the uncertainty of the source information about borrowers, which is most effective in conditions of incompleteness and uncertainty of information, the presence of linguistic variables and qualitative criteria.

To solve the tasks formulated the rules of fuzzy inference to assess the credit rating of borrowers;

- a model of credit risk assessment based on the Matlab program has been developed, which allows solving the problem of determining the level of credit rating, making intermediate and final conclusions, highlighting the strengths and weaknesses of the client, as well as making suggestions on the advisability of issuing a loan.

The developed model was tested on the basis of the Matlab program. The fuzzy logic technique is one of the most important machine learning methods used to assess credit risks. The use of fuzzy logic in the financial sector allows us to solve problems that are often influenced by many different and complex factors that cannot be labeled with a clear number or process. Despite the complexity of creating a rule base, models based on fuzzy logic can quickly and efficiently solve complex problems, which is uncharacteristic of traditional probabilistic mathematical models.