WP4: Economic Impact & Dissemination

Work Package 4: Economic Impact & Dissemination

Lead: Prof. Stephen Chan (American University of Sharjah) Duration: Months 8-12 Status: Completed

Research Context

The translation of methodological advances into tangible economic benefits represents a critical yet often overlooked dimension of financial machine learning research. While academic publications typically focus on statistical performance metrics, practitioners and policymakers require quantification of business impact in monetary terms. This work package bridges this gap by conducting rigorous economic impact analysis and establishing frameworks for knowledge transfer.

Economic Significance of Credit Risk

Credit risk management stands at the intersection of financial stability and social welfare. The 2008 financial crisis demonstrated how inadequate credit risk assessment can trigger systemic consequences (Acharya et al., 2009). At the micro level, credit decisions directly affect:

• Lenders: Portfolio losses, capital requirements, profitability
• Borrowers: Access to credit, interest rates, financial inclusion
• Society: Economic growth, wealth distribution, market efficiency

The P2P lending market, estimated at USD 67.93 billion globally in 2023 (Statista), represents a rapidly growing segment where improved risk models can yield substantial benefits across all stakeholders.

Objectives

1. Quantify economic benefits of improved credit risk prediction using established frameworks
2. Develop open-source software tools enabling practitioner adoption
3. Disseminate findings through academic conferences and publications
4. Establish sustainable framework for Swiss-Chinese research collaboration

Economic Impact Framework

Theoretical Foundation

The economic value of credit scoring improvements can be analyzed through multiple lenses:

Decision Theory Framework: Following Hand (2009), we model lending decisions as binary classifications with asymmetric costs:

where $\pi_1, \pi_0$ are class priors, $C_{FN}, C_{FP}$ are misclassification costs, and FNR, FPR are false negative and false positive rates.

Profit Maximization: The profit-based measure (Verbraken et al., 2014) captures lending economics:

where $r$ is interest rate, $L_i$ is loan amount, $D_i$ is default indicator, and LGD is loss given default.

Capital Efficiency: Under Basel regulatory frameworks, improved PD estimates reduce required capital:

where $\rho(PD)$ is the risk weight function dependent on probability of default.

Portfolio-Level Impact Analysis

Simulation Framework

We simulate economic impact using historical Bondora portfolio data with the following assumptions:

Model Comparison Results

Comparing lending decisions under different models:

Financial Impact Quantification

For a CHF 100M portfolio:

Sensitivity Analysis

Impact under varying assumptions:

Stakeholder Impact Analysis

Lender Perspective

Traditional Banks:

P2P Platforms:

Borrower Perspective

Improved credit models enable expanded financial inclusion:

The graph-based approach particularly benefits borrowers with limited traditional credit history by leveraging similarity to successful borrowers.

Systemic Perspective

At the market level, improved credit allocation yields:

1. Reduced Systemic Risk: Better borrower-lender matching reduces default correlation
2. Market Efficiency: Capital flows to highest-risk-adjusted returns
3. Financial Stability: Lower NPL ratios across the system

Software Development

Open-Source Implementation

The methodology is implemented as production-ready software:

Repository Structure:

homophily-gat-credit/
├── src/
│   ├── data/          # Data loading and preprocessing
│   ├── models/        # GNN model implementations
│   ├── training/      # Training pipelines
│   └── evaluation/    # Metrics and visualization
├── configs/           # Hyperparameter configurations
├── notebooks/         # Tutorial notebooks
├── tests/             # Unit and integration tests
└── docs/              # API documentation

Key Components:

Planned Software Releases

Dissemination Activities

Academic Conferences

Publication Pipeline

Target Journals:

Industry Engagement

Webinar Series:

• Introduction to GNNs for Credit Risk (Q1 2025)
• Hands-on Workshop: Building Graph-Based Models (Q2 2025)
• Case Study: Implementing Homophily-GAT (Q3 2025)

Technical Reports:

• White paper for fintech industry stakeholders
• Regulatory guidance document for compliance teams
• Implementation guide for data science teams

Collaboration Framework

Swiss-Chinese Partnership Evolution

The project established multi-level collaboration mechanisms:

Research Network Integration

Future Funding Applications

Industry Application Pathways

Target Market Segments

Implementation Considerations

Technical Requirements:

• Data infrastructure for graph construction
• GPU computing for model training
• API integration for real-time scoring
• Monitoring systems for model drift

Organizational Requirements:

• Data science team with GNN expertise
• Model risk management processes
• Regulatory approval workflows
• Change management support

Impact Metrics

Quantitative Outcomes

Qualitative Outcomes

1. Methodological Contribution: Novel homophily-guided graph construction for credit risk
2. Empirical Evidence: Comprehensive validation across 5 datasets, 15 methods
3. Economic Quantification: Framework for translating ML improvements to business value
4. Network Building: Strengthened Swiss-Chinese academic ties
5. Capacity Building: PhD researchers trained in cutting-edge methodologies

Project Legacy

Sustainable Research Infrastructure

The project leaves lasting infrastructure:

1. Open-Source Code: Publicly available, documented, maintained
2. Benchmark Suite: Reproducible experiments for future research
3. Collaboration Network: Active partnerships for ongoing work
4. Training Materials: Tutorials, workshops, documentation
5. Funding Pipeline: Multiple applications in progress

Knowledge Transfer Mechanisms

References

• Acharya, V. V., et al. (2009). The financial crisis of 2007-2009: Causes and remedies. Financial Markets, Institutions & Instruments, 18(2), 89-137.
• Hand, D. J. (2009). Measuring classifier performance: a coherent alternative to the area under the ROC curve. Machine Learning, 77(1), 103-123.
• Verbraken, T., et al. (2014). Development and application of consumer credit scoring models using profit-based classification measures. European Journal of Operational Research, 238(2), 505-513.

Related Publications

• Liu, Y., Baals, L. J., Osterrieder, J., & Hadji-Misheva, B. (2024). Leveraging Network Topology for Credit Risk Assessment in P2P Lending. Expert Systems with Applications.
• Liu, Y., Baals, L. J., Osterrieder, J., & Hadji-Misheva, B. (2024). Network Centrality and Credit Risk. Finance Research Letters.
• Chan, S., et al. (2024). Stylized Facts of Metaverse Non-Fungible Tokens. Physica A.
• Chu, J., et al. (2025). Cryptocurrency in War: A Double-Edged Sword? Applied Economics Letters.
• Chen, Y., et al. (2025). Multilayer Topology-Aware Graph Contrastive Learning. JRSS-A.

Contact for Collaboration

(c) Joerg Osterrieder 2025