Author: Jakub Schrimpel, Data Analyst Intern at FNA and G20 Competition runner-up.

This is the first article in a series on bank complexity and systemic risk. The author has used FNA’s Supervisory Technology (Suptech) to construct multiple dashboards with the help of LEI data that can aid regulators when evaluating the geographical complexity of a bank. 

The issue of bank complexity has received increased attention from academics, regulators, and media outlets since the crisis. The question “Can a bank be too complex to fail?” was addressed in a clear affirmative with a range of policies –  breaking up and separation of the institution by the business lines, limiting the cross-border size dimension, enhanced capital and liquidity requirements for GSIBs, different strategies for resolution and legal entity rationalisation.

Why do we need to understand the complexity? 

The more complex a bank becomes, the more time and money it costs to understand it and – in case of its failure –  to resolve it. In other words, the greater the complexity of a bank holding company, the harder it is to disentangle and understand its interconnectedness. This, in turn, increases the likelihood of the bank’s activities and relationships between its subsidiaries going unnoticed, and hence harder to resolve in times of financial distress.

This creates costs that are borne by creditors. For example, after the collapse of the Lehman Brothers, the task for multiple regulators to coordinate across multiple unharmonised jurisdictions was daunting, and it took years to unwind 2985 legal entities spanning through 50 countries worth 600 billion dollars in interconnected portfolios, despite Barclays agreeing to absorb its core businesses shortly after the collapse. As the group has entered the resolution with more separate entities, the concern value of the whole group has diminished.

When mapping the geographical complexity of legal entities (as in Basel Committee on Banking Supervision’ corporate governance principles or UK’s FCA requirements), the burden is usually shifted to the bank. This mapping exercise is time-intensive and costly, as the construction and maintenance of legal entity information involve textual research, data creation, extraction and transformation, and migration to one server.

LEI data 

Since established by the FSB and used by regulators across the world, Global Legal Entity Identifier Foundation (GLEIF) provides historical and present data per entity in the form of Legal Entity Identifier (“LEI”). This identifier provides information about both the entities’ key legal information (Figure 1) and their ownership structures by linking them to the direct accounting consolidating parent and the ultimate consolidating parent (Figure 2). In a nutshell, the data can answer the questions “who is who” and “who owns whom”.

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Figure 1 – LEI level 1 data example, containing key legal information of the entity

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Figure 2 – LEI level 2 data, providing the information that connects the entities together into an ownership structure

How can Suptech & Network Analytics help?

FNA has created a LEI visualisation dashboard that allows regulators to analyse ownership structures. First, we create the links between the parent-subsidiary structures, going from the top-level parent (holding company) to lower levels of organisational structure (Figure 3), ultimately creating a network of the bank holding company (Figure 4).

Moreover, the evolution of the network can be tracked over time by the regulators on a number of complexity metrics, providing insights into their decision-making. In this link, find the dashboard of the network structure of Banco Santander and follow our next blog, where we will show how the insights from the other metrics tracked will tell a story of a bank.

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Figure 3 – visualised ownership structure of Spanish holding Banco Santander and its American subsidiary Santander USA and Chrysler Capital Auto Funding

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Figure 4 – Changing ownership structure of Banco Santander

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How to track bank complexity with network analytics?

Author: Jakub Schrimpel, Data Analyst Intern at FNA and G20 Competition runner-up. This is the first article in a series on bank complexity and systemic risk. The author has used FNA’s Supervisory Technology (Suptech) to construct multiple dashboards with the help of LEI data that can aid regulators when evaluating the geographical complexity of a […]

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