# Leaders in the global banking network: Analysis of the Bank for International Settlements network data

## TL;DR

This paper introduces a new method to identify influential and vulnerable countries in the global banking network using BIS data from 2000 to 2015.

## Contribution

The paper introduces an adversarial network-based method to identify low-key leaders and highly exposed nodes in the global banking network.

## Key findings

- Low-key leaders like the United States and Mexico have strong influence with lower exposure to contagion.
- Highly exposed nodes such as Germany and the UK after 2003 have broad lending portfolios that increase vulnerability.
- The United States lost its low-key leader status after the 2008 financial crisis.

## Abstract

Prior work on networks derived from the Bank for International Settlements (or BIS) focused on centrality measures such as degree, betweenness, and DebtRank, with less attention given to adversarial network models. In this work, we address this gap by introducing an adversarial network-based method to locate influential countries within the global banking network. We analyze BIS data from 2000 to 2015, modeling countries as nodes and lending relationships as weighted, directed edges. We study low-key leaders, which are countries with outsize influence despite lower centrality, and highly exposed nodes, which are countries most vulnerable to defaults. Using the Common Out-neighbor (or CON) score with PageRank, we quantify each country’s influence and exposure, and define a measure of low-key leader strength. Our results show that low-key leaders, such as those in the United States and Mexico, possess strong influence with lower exposure to contagion, whereas highly exposed leaders, like those in Germany and the United Kingdom after 2003, maintain broad lending portfolios that heighten vulnerability. We also examine these roles over time, including the United States’ loss of low-key leader status after the 2008 financial crisis. Our analysis of low-key leaders and highly-exposed nodes provides new insights into systemic risk within the BIS network.