Resources
Research Resources
This page provides access to tools, data, and resources developed as part of the SNSF Leading House Asia research project.
Software & Tools
R Shiny Dashboard
Status: Completed Release: December 2024
An interactive dashboard for consumer lending credit risk analysis featuring:
- Real-time data analysis capabilities
- Graph-based model visualization
- Prediction interface for lending decisions
- Comparison with baseline models
Code Repository
GitHub: Digital-AI-Finance/SNFS-Leading-House-Asia Organization: Digital-AI-Finance
Project documentation, website source, and research methodology available on GitHub. The repository includes conference materials, publications, and the complete project website.
Data Sources
Consumer Lending Data
The project utilizes data from various Chinese consumer finance platforms:
| Source Type | Description |
|---|---|
| Internet-based consumer finance | Ant Group, Tencent, JD.com platforms |
| Commercial banks | Traditional banking consumer credit data |
| Licensed consumer finance companies | Regulated lending institution data |
Note: All data is anonymized and processed in compliance with data protection regulations.
Research Methodology
Graph-Theoretic Analysis Framework
Our methodology consists of four key components:
- Data Collection and Processing
- Comprehensive consumer lending data collection
- Privacy-compliant anonymization
- Feature extraction and preprocessing
- Graph Construction
- Network modeling of consumer interactions
- Transactional behavior mapping
- Similarity-based edge creation
- GNN Model Development
- Graph Neural Network architecture design
- Homophily-guided graph construction
- Default prediction optimization
- Validation and Economic Analysis
- Historical data backtesting
- False positive/negative rate analysis
- Financial benefit quantification
Publications & Presentations
Key Research Papers
- Credit Risk Prediction via GNN (JMIS submission)
- P2P Lending Platform Analysis
- Fraud Detection in Ethereum
Conference Materials
- EcoSta 2025 Invited Session slides
- ICSA 2024 China presentation
- AUS ICMS 2025 materials
External Resources
Graph Neural Networks
| Resource | Description |
|---|---|
| PyTorch Geometric | GNN library |
| DGL | Deep Graph Library |
| NetworkX | Network analysis in Python |
Credit Risk Literature
| Topic | Reference |
|---|---|
| Traditional scoring | FICO, Altman Z-Score |
| Machine learning | XGBoost, Random Forest |
| Deep learning | Neural network approaches |
| Graph-based | GNN, GAT, GCN methods |
China Consumer Finance
| Resource | Description |
|---|---|
| Ant Group | Largest consumer finance platform |
| Tencent WeBank | Digital banking services |
| JD Finance | E-commerce integrated lending |
Collaboration Resources
Partner Institutions
Research Networks
COST Action CA19130 - Fintech and AI in Finance
European research network with 300+ researchers from 51 countries. Prof. Osterrieder serves as Action Chair. Key 2024 activities:
- Brussels Meeting at European Commission (May 14, 2024)
- PhD School on Generative AI in Finance (2024)
- Book Call: "Transparency in FinTech" (World Scientific)
MSCA Digital Finance Network
Marie Sklodowska-Curie Industrial Doctoral Network (2024-2027). 17 fully funded PhD positions across 15 institutions in 12 countries. Builds on COST Action CA19130 collaborations. Prof. Chu joined as associated partner in 2024.
SNSF Network-based Credit Risk Project
Swiss National Science Foundation grant for "Network-based credit risk models in P2P lending markets" (Aug 2022 - Aug 2025). CHF 347,836 funding.
Contact
For questions about research resources or collaboration opportunities:
- Prof. Joerg Osterrieder: joerg.osterrieder@utwente.nl
- Prof. Jeffrey Chu: jeffrey.jchu@ruc.edu.cn
- GitHub: SNFS-Leading-House-Asia
(c) Joerg Osterrieder 2025