Enhancing Credit Risk Decision-Making in Supply Chain Finance With Interpretable Machine Learning Model
The increasing complexity of supply chain finance poses significant challenges to effective credit risk assessment. Traditional black-box models often fail to provide insights into the factors driving credit risk, which is essential for stakeholders when making informed decisions. By conducting anal...
Saved in:
Main Authors: | Guanglan Zhou, Shiru Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10843707/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Novel Hybrid Model for Credit Risk Assessment of Supply Chain Finance Based on Topological Data Analysis and Graph Neural Network
by: Kosar Farajpour Mojdehi, et al.
Published: (2025-01-01) -
Loan interest rates, credit guarantees, and lifestyle on credit making decisions at financing companies
by: Hendri Herman, et al.
Published: (2023-12-01) -
Machine learning prediction of obesity-associated gut microbiota: identifying Bifidobacterium pseudocatenulatum as a potential therapeutic target
by: Hao Wu, et al.
Published: (2025-02-01) -
Designing a causal model of factors affecting the Lars supply chain
by: mojtaba khalesi, et al.
Published: (2024-09-01) -
ADOPTION OF ARTIFICIAL INTELLIGENCE AND DIGITAL SUPPLY CHAIN FOR ENHANCING SUPPLY CHAIN PERFORMANCE: MEDIATING ROLE OF GREEN SUPPLY CHAIN PROCESS
by: Abdulaziz Aljoghaiman, et al.
Published: (2024-12-01)