A Two-Stage Hybrid Default Discriminant Model Based on Deep Forest
<b>Background:</b> the credit scoring model is an effective tool for banks and other financial institutions to distinguish potential default borrowers. The credit scoring model represented by machine learning methods such as deep learning performs well in terms of the accuracy of default...
Main Authors: | Gang Li, Hong-Dong Ma, Rong-Yue Liu, Meng-Di Shen, Ke-Xin Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-05-01
|
Series: | Entropy |
Subjects: | |
Online Access: | https://www.mdpi.com/1099-4300/23/5/582 |
Similar Items
-
Credit risk and loan default among Ghanaian banks: An exploratory study
by: Matthew Ntow-Gyamfi, et al.
Published: (2013-03-01) -
ANALYSIS OF FACTORS INFLUENCING LOAN DEFAULT AMONG POULTRY FARMERS IN OGUN STATE NIGERIA
by: O Oni, et al.
Published: (2006-07-01) -
Credit default swaps (CDS) and loan financing
by: Shan, Chenyu., et al.
Published: (2013) -
DETERMINANTS OF AGRICULTURE-RELATED LOAN DEFAULT: EVIDENCE FROM CHINA
by: Zhichao Yin, et al.
Published: (2020-01-01) -
Time to Default in Credit Scoring Using Survival Analysis
by: Ana Maria SANDICA, et al.
Published: (2017-12-01)