The Case of the Global Legal Entity Identi fi er System
9.4 Empirical Account .1 The Data Quality Controversy
Our examination of the development of the LEI IDI used interviews with key actors involved in the process and an analysis of documents relating to the development of the LEI and the documentation relating to the establishment of the Global Legal Entity Identifier System (GLEIS) from the sponsors of the GLEIF identification infrastructure such as the Regulatory Oversight Committee (ROC), the Commodities and Futures Trading Commission (CFTC), the Securities and Exchanges Commission (SEC), the Financial Stability Board (FSB), the G-20, and the Global Legal Entity Identifier Foundation (GLEIF). In addition to interviews and documents, the authors also undertook a day of participant observations at one of the LOUs responsible for issuing LEIs to entities applying for an identifi-cation number in order to understand better and at first-hand the issuing, verification, and validation process associated with the LEI.
The analytical framework used relies on the identification and tracing of key controversies (Marres 2004; Panourgias 2015) that arise during the development of regulatory regimes. In this chapter, we focus on the controversy revolving around the quality of the data.
Having identified this controversy as key to understanding the development of LEI, we grouped relevant issues raised by the actors regarding that controversy.
Among the central issues that relate to data quality were debates regarding the cost of adoption of the LEI, costs of its continued use and maintenance, the relative lack of incentives to use LEI by commercial actors and concerns about the effectiveness of the regulatory action that would be enabled by the use of the LEI in the reporting offinancial markets transactions.
9.4.2 Data Quality and the Evolution of the GLEIS
In the global LEI system, anyone can apply to become a LEI Operating Unit (LOU), which issues LEIs, subject to approval by the LEI foundation and com-pliance with the foundation’s master and service-level agreements (Millo et al.
2019). Once approved and operational, the LEI Operating Unit aims to verify the accuracy and validity of the data that applicants provide (illustrated in Figure 9.2) against the records of at least one local authoritative source, typically, the relevant business register of the applicant entity’s jurisdiction.
Once the data has been verified by the LOU and a LEI number has been issued, there still remains the critical task of ensuring that the data provided remains current and also that any inaccuracies that may exist in the records of the local authoritative sources (e.g. a national corporate register) are picked-up and
corrected. This is done through a challenge process (illustrated in Figure 9.3) through which a challenger—usually another party (intermediary or counterpart) in a transaction—submits a challenge to the published LEI data of an entity by providing evidence for the challenge to the LOU and which the LOU is then obliged to investigate and then if the investigation upholds the challenge, the original data is amended as necessary.
Self Registration
LEI issuer
Verification
Local authoritative
source
Compliant LEI number LEI applicant
Provides legal entity
data
Issues compliant
LEI
Figure 9.2 The LEI issuing process.
Source: GLEIF.
Self-entry via gleif.org
LEI issuer
Checks
Local authoritative
source
Compliant LEI number Challenger
Provides evidence for
challenge
Updates of confirms
record
LEI number holder
Figure 9.3 Improving data accuracy through the LEI challenge function.
Source: GLEIF.
Both the initial quality verification of data and the challenge process depend on a chain of relations between the entity applying for a LEI, the LEI Operating Unit, and the GLEIF, which oversees the overall process. These relations are illustrated in Figure 9.4, and are a key aspect of the process by which the usefulness and value LEI data is increased. Crucially, these are dependent on the GLEIF and its view of quality.
A key element in this process is the way data quality is defined across the different groups of actors who take part in the infrastructure. Participants we interviewed held differing views of what constituted‘data quality’, how this quality may or may not be measured, and what implications the lack of quality might have. These differing views constituted a challenge to the GLEIF, motivating it to develop and propose a unified methodology for assessing data quality, to overcome the different views.
The methodology developed by the GLEIF for measuring the quality of the identification data associated with the LEIs issued provides for different criteria of data quality (see Table 9.1). Underpinning each of these criteria are numerous checks (more than 200 in total) that measure different aspects of the data.
Critically, the criteria are also associated with thresholds each LEI Operating Units has to meet so that the LEIs that it manages would be considered of sufficient quality. Besides, the data quality scores for the LEI Operating Units are published monthly on GLEIF’s website.
The data quality assessment is tied to legal agreements the Foundations has with the LEI Operating Units. These arrangements, in effect, make the LEI
GLEIS Figure 9.4 Data quality control process.
Source: GLEIF.
Operating Units responsible for both the performance of the LEI applicants in terms of the maintenance of their company data and, indirectly, of the underlying authoritative sources they use in order to verify the data. In this way, the influence of the Foundation extends beyond its immediate relations to reach throughout the entire infrastructure and even beyond. For example, the business registers, which are not part of the infrastructure but provide thefirst authoritative source against which an applicant’s data is checked, have come under pressure to improve the quality of their data as the LEI challenge facility reveals problems and inconsist-encies in the data they hold on companies. Furthermore, through some forward-looking features of the data quality methodology such as‘Quality Maturity Level’
the Foundation also directs future action of the LEI Operating Units towards attaining further desirable data quality criteria. The overall impact of integrating data quality measures into the IDI contributes to making GLEIF the sole arbiter of the performance of the LEI Operating Units in terms of the quality of the data they collect during the LEI issuing process.