Credit decision system based on combination weight and eXtreme Gradient Boosting algorithm

Autor:	Chen Youlve, Chen Jiangtian, Bi Kaiyun
Rok vydání:	2021
Předmět:	Entropy weight method Credit risk assessment History Bank credit Index (economics) Computer science Decision system Principal component analysis Extreme gradient boosting Algorithm Computer Science Applications Education Credit risk
Zdroj:	Journal of Physics: Conference Series. 1955:012081
ISSN:	1742-6596 1742-6588
Popis:	According to the demand of bank credit business, a credit decision system is established. The system mainly uses principal component analysis and eXtreme Gradient Boosting algorithm to establish a credit risk assessment model. When calculating the index weight, it proposes a more accurate method: combination weight method, which combines the advantages of entropy weight method and expert scoring method to calculate the relatively accurate weight. Then, the credit risk of each enterprise is analyzed quantitatively. Through the results, we give the corresponding credit strategies of banks in different situations.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::5cb1067e13730e6a781c31e22ea07f59 https://doi.org/10.1088/1742-6596/1955/1/012081 Zobrazit plný text záznamu