Monitoring bank risk around the world using unsupervised learning
本文构建了一个透明且动态的决策支持工具，利用无监督学习算法对全球上市银行按风险程度进行聚类和排序，帮助识别脆弱区域。
This paper provides a transparent and dynamic decision support tool that ranks clusters of listed banks worldwide by riskiness. It is designed to be flexible in updating and editing the values and quantities of banks, indicators, and clusters. For constructing this tool, a large set of stand-alone and systemic risk indicators are computed and reduced to fewer representative factors. These factors are set as features for an adjusted version of a nested k-means algorithm that handles missing data. This algorithm gathers banks per clusters of riskiness and ranks them. The results of the individual banks' multidimensional clustering are also aggregable per country and region, enabling the identification of areas of fragility. Empirically, we rank five clusters of 256 listed banks and compute 72 indicators, which are reduced to 12 components based on 10 main factors, over the 2004–2024 period. The findings emphasize the importance of giving special consideration to the ambiguous impact of banks' size on systemic risk measures.