finding illicit financial flowS with network science

Polarization // social networks

Inequality and crime // financial networks

who is the largest investor in spain?

who is the largest investor in brazil?

who is the largest investor in south africa?

why?

professionals

corporate entities

state

PARt 1: The corporations

corporate ownership is complex

how do companies organize their corporate structures?

Garcia-Bernardo, J., Fichtner, J., Takes, F. W., & Heemskerk, E. M. (2017). Uncovering offshore financial centers: Conduits and sinks in the global corporate ownership network. Scientific Reports, 7(1), 1-10.

Data provider: Orbis (300M+ companies, 100M+ ownership links)

sinks

Garcia-Bernardo, Fichtner, Takes, Heemskerk (2017)

conduits

Work with Valeria Secchini (PhD candidate at COPTAX, Charles University)

modeling corporate structures with higher-order dependencies

Work with Valeria Secchini (PhD candidate at COPTAX, Charles University)

modeling corporate structures with higher-order dependencies

PH

PL

Problem: overfitting

(high number of false positives)

On realistic synthetic data:

Precision: 7%

Sensitivity: 62%

Our solution: Cross-validation

Find pattern in train dataset, keep if it holds in validation

Results:

Applied to multinational corporations

201 patterns, 80% containing a tax haven

p.

p.

p.

PARt 2: the professionals

• Corporate structures are created by tax professionals:
  • Set up the entity
  • Provide directors
  • Manage the accounts
• In the Netherlands: Trust Industry (trustkantoren)
  • 94% of the services are provided to foreign companies
  • 250,592 entities in 100 addresses
• Providing trust services requires a license
  • But it is expensive + compliance

facilitators are key

detect them based on their network

Garcia-Bernardo, Javier, Joost Witteman, and Marilou Vlaanderen. "Uncovering the size of the illegal corporate service provider industry in the Netherlands: a network approach." EPJ Data Science 11.1 (2022): 23.

Results:

Illegal trust service providers:

• 31-51% of total number of trust service providers
• 19-27% of all companies managed

Maybe a better approach: Graph Neural Networks

Andrea Longobucco MSc

https://distill.pub/2021/gnn-intro/

PARt 3: The state

what type of country lose the most from tax avoidance?

Garcia-Bernardo, J., Haberly, D., Janský, P., Palanský, M., & Secchini, V. (2022). The indirect costs of corporate tax avoidance exacerbate cross-country inequality (No. 2022/33). WIDER Working Paper.

Garcia-Bernardo, J., Haberly, D., Janský, P., Palanský, M., & Secchini, V. (2022). The indirect costs of corporate tax avoidance exacerbate cross-country inequality (No. 2022/33). WIDER Working Paper.

Garcia-Bernardo, J., Haberly, D., Janský, P., Palanský, M., & Secchini, V. (2022). The indirect costs of corporate tax avoidance exacerbate cross-country inequality (No. 2022/33). WIDER Working Paper.

Garcia-Bernardo, J., Haberly, D., Janský, P., Palanský, M., & Secchini, V. (2022). The indirect costs of corporate tax avoidance exacerbate cross-country inequality (No. 2022/33). WIDER Working Paper.

Garcia-Bernardo, J., Haberly, D., Janský, P., Palanský, M., & Secchini, V. (2022). The indirect costs of corporate tax avoidance exacerbate cross-country inequality (No. 2022/33). WIDER Working Paper.

 summary

• Wealth inequality is rising
• Tax avoidance is key
• Complex corporate structures are used for it

• Network science tools help us:
  • Understand how they organize (higher order dependencies)
  • Estimate which countries gain/lose
  • Detect facilitators

 summary

• Wealth inequality is rising
• Tax avoidance is key
• Complex corporate structures are used for it

• Network science tools help us:
  • Understand how companies organize (higher order dependencies)
  • Estimate which countries gain/lose
  • Detect facilitators

Javier García-Bernardo

javier.gbe@pm.me