论文标题
多层网络分析可改善信用风险预测
Multilayer Network Analysis for Improved Credit Risk Prediction
论文作者
论文摘要
我们提出了用于信用风险评估的多层网络模型。我们的模型说明了借款人之间的多个联系（例如其地理位置和经济活动），并允许明确建模连接借款人之间的相互作用。我们开发了多层个性化的Pagerank算法，该算法允许量化网络中任何借款人默认暴露的强度。我们在农业贷款框架中测试我们的方法论，当借款人遭受相同的结构风险时，人们一直怀疑它长期存在默认值。我们的结果表明，仅通过将中心性多层网络信息包括在模型中，而这些收益通过更复杂的信息（例如多层pagerank变量）增加。结果表明，当一个人连接到许多违法者时，默认风险是最高的，但是这种风险会因个人的大小而减轻，这既显示默认风险，又显示了整个网络中的财务稳定性。
We present a multilayer network model for credit risk assessment. Our model accounts for multiple connections between borrowers (such as their geographic location and their economic activity) and allows for explicitly modelling the interaction between connected borrowers. We develop a multilayer personalized PageRank algorithm that allows quantifying the strength of the default exposure of any borrower in the network. We test our methodology in an agricultural lending framework, where it has been suspected for a long time default correlates between borrowers when they are subject to the same structural risks. Our results show there are significant predictive gains just by including centrality multilayer network information in the model, and these gains are increased by more complex information such as the multilayer PageRank variables. The results suggest default risk is highest when an individual is connected to many defaulters, but this risk is mitigated by the size of the neighbourhood of the individual, showing both default risk and financial stability propagate throughout the network.
