Background: 
The consumer lending market in China, particularly the alternative lending sector, is projected to grow substantially, reaching US$ 258.51 billion in 2023, with a further increase to US$ 347.72 billion by 2027[1]. A key player in this market is the 'Buy Now, Pay Later' (BNPL) segment, which, although still in its nascent stages, shows robust growth prospects. This trend is supported by the widespread use of electronic payment systems and the popularity of online shopping platforms like Alibaba's Taobao. For instance, the emergence of products like Ant Credit (蚂蚁信贷), offered by Ant Group, a part of Alibaba, highlights the integration of credit services into everyday e-commerce activities. Consumers can make purchases on Taobao and defer payments, illustrating a shift towards more flexible and accessible credit options[2]. The considerable size of this market, coupled with the expansion of segments such as BNPL, highlights the substantial economic potential achievable through enhanced precision in predicting defaults within consumer lending.

Principal project components:
1. Integration of Swiss Analytical Techniques with Chinese Market Data: This component involves the application of advanced graph-theoretic methods developed by Swiss research institutions, tailored to analyze China's consumer lending data. This collaboration represents a significant application of Swiss analytical expertise in a real-world context, bridging cutting-edge European research with practical challenges in Asia's financial sector. 
2. Risk Assessment and Decision-Making Framework: The project will create an advanced framework for assessing credit risk using the graph-based models. This will aid in identifying potential default risks more accurately, enabling lenders to make more informed and strategic lending decisions, thereby reducing the rate of bad loans and enhancing the overall health of the consumer lending market. 
3. Analysis on Economic Benefits of Improved Prediction Accuracy: Increasing the precision of default predictions will have substantial economic benefits. By reducing false positives, we can lower opportunity costs and capture more lending interest revenue. Simultaneously, decreasing false negatives will minimize default losses. We will quantify the financial benefit earned by the improvement of model prediction.
 
Aims and goals:
1. Enhance Credit Risk Prediction Accuracy: To significantly improve the precision of default predictions in China's consumer lending market by employing advanced graph-theoretic techniques, leading to more reliable credit risk assessments.
2. Foster Swiss-Asian Research Collaboration: To establish a successful model of collaboration between Swiss research institutions and Chinese market practitioners, showcasing the application of Swiss analytical methodologies in a practical, real-world setting within Asia's financial landscape.
3. Promote Sustainable Consumer Lending Practices: By improving risk assessment processes, the goal is to encourage responsible lending, reduce the incidence of non-performing loans, and support the sustainable growth of the consumer credit market in China.

Methodology: 
Data Collection and Processing
​Graph-Theoretic Analysis
Validation and Iteration
Analysis on Economic Benefits of Improved Prediction Accuracy​