A Novel Hybrid Model for Loan Default Prediction in Maritime Finance Based on Topological Data Analysis and Machine Learning
The global shipping industry, pivotal to international trade, faces inherent financial vulnerabilities due to cyclical asset values, volatile freight rates, and high leverage, rendering traditional credit risk models inadequate. This study proposes a novel framework integrating topological data anal...
Saved in:
| Main Authors: | Mohammad Amin Kheneifar, Babak Amiri |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10981721/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Data-Driven Loan Default Prediction: A Machine Learning Approach for Enhancing Business Process Management
by: Xinyu Zhang, et al.
Published: (2025-07-01) -
Predicting mortgage credit defaults in morocco using machine learning approaches
by: Amine Hade, et al.
Published: (2025-06-01) -
Comparative Analysis of RF, SVR with Gaussian Kernel and LSTM for Predicting Loan Defaults
by: Konstantinos Kofidis, et al.
Published: (2024-11-01) -
Predicting financial default risks: A machine learning approach using smartphone data
by: Shinta Palupi, et al.
Published: (2024-11-01) -
Definition and essence of the concept of «default» in scientific literature
by: I.M.
Published: (2023-07-01)