БАГАТОФАКТОРНА ОЦІНКА КРЕДИТОСПРОМОЖНОСТІ ФІЗИЧНИХ ОСІБ ЗА ДОПОМОГОЮ ІНТЕГРАЦІЇ ЕКСПЕРТНИХ ЗНАНЬ
The observed trend of active development of consumer, educational, mortgage and other crediting, along with high competition in the modern crediting market, makes it necessary to pay even more attention to mathematical modeling of the assessment of the current and future solvency of natural persons,...
Gespeichert in:
| Datum: | 2020 |
|---|---|
| 1. Verfasser: | |
| Format: | Artikel |
| Sprache: | English |
| Veröffentlicht: |
V.M. Glushkov Institute of Cybernetics of NAS of Ukraine
2020
|
| Schlagworte: | |
| Online Zugang: | https://jais.net.ua/index.php/files/article/view/459 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Назва журналу: | Problems of Control and Informatics |
Institution
Problems of Control and Informatics| Zusammenfassung: | The observed trend of active development of consumer, educational, mortgage and other crediting, along with high competition in the modern crediting market, makes it necessary to pay even more attention to mathematical modeling of the assessment of the current and future solvency of natural persons, and hence the credit risk management process of a commercial bank directed to reduce losses associated with a significant increase in overdue loans. The article presents approaches to the assessment of the solvency of natural persons, based on the analysis of factors related to the relations of lenders and borrowers on the parameters of their solvency and reliability based on multi-factor expert, neural network and fuzzy types of modeling. The developed tools can serve as the basis of a management decision support system in financial institutions, it is distinguished by the ability to reliably highlight the characteristics of a potential client related to the high risk area. Within the framework of this tools, a method of balanced multicriteria credit assessment of natural persons is proposed, which provides for the compilation of expert estimates regarding the priority of indicators of credit worthiness, in general, and current indicators of solvency of natural persons, in particular. The compilation of acquired expertise is carried out by means of neural network modeling and the fuzzy maximin convolution method of qualitative criteria for assessing the solvency of natural persons. Selected assessment criteria of natural persons solvency are differentiated by degrees of their priorities, which at the initial stage of the examination are identified on the basis of an agreed opinion of specially invited experts in the form of generalized weights of their relative influence on the solvency level of the natural persons. |
|---|