Risk Analysis of Project Financing Using the Logical–linguistic Classification Method

Problem statement: The assessment of credit risks is predictive in nature, associated with the uncertainty of the influence of many factors that cannot be accurately described mathematically. This leads to a low probability of obtaining and repayment of loans for various facilities, lending including specialized. There are usually two main types of risk assessment methods—qualitative and quantitative. Based on these methods, it is possible to identify a scenario approach using the methods of fuzzy set theory to calculate the values of membership functions. However, the problem of ranking the scenarios for the implementation of the project for which a loan is requested has not been sufficiently studied. The purpose: Analysis of specialized exposures on the example of project financing using methods of logical–linguistic classification and ranking method. Building a database of criteria for credit requirements for project financing and developing an algorithm for calculating the risk assessment of lending to the analyzed project. Results: The developed algorithm makes it possible to increase the reliability and speed of evaluation of the analyzed project to be financed. According to this method, the possibility of non-repayment of the loan can be predictable. Practical significance: The results of the study can be used in the development of a computer program that allows you to speed up the risk analysis of credit projects.