network-based-credit-risk-models
Network-Based Credit Risk Models in P2P Lending Markets - SNSF Research Project
Information
| Property | Value |
|---|---|
| Language | Jupyter Notebook |
| Stars | 0 |
| Forks | 0 |
| Watchers | 0 |
| Open Issues | 59 |
| License | MIT License |
| Created | 2025-12-01 |
| Last Updated | 2026-03-25 |
| Last Push | 2026-02-16 |
| Contributors | 3 |
| Default Branch | main |
| Visibility | private |
Notebooks
This repository contains 9 notebook(s):
| Notebook | Language | Type |
|---|---|---|
| 0.1_data_preprocessing | PYTHON | jupyter |
| 0.2_descriptive_statistics | PYTHON | jupyter |
| 1_2023.01.05 Data_Pre-processing_&_Models_training | PYTHON | jupyter |
| 2023.04.22 SNF P2P Credit Risk Auto ML | PYTHON | jupyter |
| 2_2023.07.05_Models_analysis | PYTHON | jupyter |
| 3_2023.06.28 Model Re-training and testing | PYTHON | jupyter |
| 4_2023.06.28 SHAP Explainability | PYTHON | jupyter |
| 4_2023.07.05_Models_analysis | PYTHON | jupyter |
| Auto_R_I_XAI_R2_RD_Tree_v010 | PYTHON | jupyter |
Datasets
This repository includes 12 dataset(s):
| Dataset | Format | Size |
|---|---|---|
| navigation.json | .json | 0.97 KB |
| news.json | .json | 1.22 KB |
| phd_publications.json | .json | 19.44 KB |
| project_analysis.json | .json | 10.49 KB |
| publications.json | .json | 29.89 KB |
| research_outputs.json | .json | 43.61 KB |
| site_review.json | .json | 13.24 KB |
| snsf_project.json | .json | 7.17 KB |
| snsf_project_complete.json | .json | 16.78 KB |
| team.json | .json | 2.94 KB |
| search-index.json | .json | 1.99 KB |
| project-template.json | .json | 13.44 KB |
Reproducibility
No specific reproducibility files found.
Status
- Issues: Enabled
- Wiki: Enabled
- Pages: Enabled
README
Network-Based Credit Risk Models in P2P Lending Markets
SNSF Research Project - Swiss National Science Foundation
Overview
This research project focuses on developing advanced, interpretable credit risk models tailored specifically to the needs of Peer-to-Peer (P2P) lending markets. The project addresses the unique challenges of P2P lending, such as higher information asymmetry, less regulation, and increased risk during economic downturns.
Team
Principal Investigator: Joerg Osterrieder (Bern Business School, Switzerland / University of Twente, Netherlands)
Team Members: - Lennart John Baals (Bern Business School / University of Twente) - Branka Hadji Misheva (Bern Business School) - Yiting Liu (Bern Business School / University of Twente)
Key Publications
-
Network centrality and credit risk: A comprehensive analysis of peer-to-peer lending dynamics Liu, Y., Baals, L. J., Osterrieder, J., & Hadji-Misheva, B. (2024). Finance Research Letters, 63, 105308.
-
Leveraging network topology for credit risk assessment in P2P lending: A comparative study under the lens of machine learning Liu, Y., Baals, L. J., Osterrieder, J., & Hadji-Misheva, B. (2024). Expert Systems with Applications, 252, 124100.
Collaborations
- American University of Sharjah, UAE (Prof. Dr. Stephen Chan)
- University of Manchester, UK (Dr. Yuanyuan Zhang)
- Renmin University, China (Prof. Dr. Jeffrey Chu)
- COST Action CA19130 - Fintech and Artificial Intelligence in Finance
- MSCA Industrial Doctoral Network on Digital Finance
More Information
See the Wiki for complete project information.
Source: digital-finance-msca.com
Description
Network-Based Credit Risk Models in P2P Lending Markets - SNSF Research Project
Installation
git clone https://github.com/Digital-AI-Finance/network-based-credit-risk-models.git
cd network-based-credit-risk-models
pip install -r requirements.txt
Usage
See the repository contents for usage examples.
(c) Joerg Osterrieder 2025