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Distributed company control in company shareholding graphs

Abstract:
The Company Control Problem is of central importance to banks, financial intermediaries, financial intelligence units, regulatory and supervisory authorities such as the Central Banks. It consists in understanding who takes decisions in a large company network, that is, who controls the majority of votes for each single company. This has an impact on a large number of business areas, with examples including evaluation of creditworthiness, economic analysis of the control dispersion, anti-money laundering, prevention of potentially hostile takeovers, evaluation of risks, and shock propagation.This paper is based on our experience with the Central Bank of Italy and presents an approach to the solution of the company control problem in distributed settings, especially relevant, as large and distributed ownership graphs reflect European-size applications where scalability is paramount.In particular, we formalize the problem as query answering on a large distributed database. We study how independent subqueries can be executed in each partition and the partial results assembled at a master site to produce the answer. We study the formal properties of the problem, that is not easily parallelizable, and then present a method that supports parallelism at best.We present a thorough experimental evaluation of our approach with the Italian company graph of the Bank of Italy and the European Register of Financial Intermediaries and Affiliates as well as many artificial graphs to fully assess scalability.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/ICDE51399.2021.00294

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author


Publisher:
IEEE
Host title:
2021 IEEE 37th International Conference on Data Engineering (ICDE)
Pages:
2637-2648
Publication date:
2021-06-22
Acceptance date:
2021-02-09
Event title:
37th IEEE International Conference on Data Engineering (ICDE)
Event location:
Chania, Crete, Greece (Virtual event)
Event website:
https://icde2021.gr/
Event start date:
2021-04-19
Event end date:
2021-03-21
DOI:
EISSN:
2375-026X
ISSN:
1063-6382
EISBN:
9781728191843
ISBN:
9781728191850


Language:
English
Keywords:
Pubs id:
1173365
Local pid:
pubs:1173365
Deposit date:
2021-04-25
ARK identifier:

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