Transforming Data into Opportunities: Metric of Month – Bulk Challenges
High-quality data is more than a benchmark – it is a strategic necessity. Zornitsa Manolova, Head of Data Quality Management and Data Science at GLEIF, focuses this month on ‘Bulk Challenges', a new feature in the Global LEI System. Resolving challenges at scale improves the timeliness, accuracy, and reliability of LEI records – safeguarding the integrity of the LEI ecosystem
Author: Zornitsa Manolova
Date: 2025-05-08
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In an increasingly interconnected global economy, the ability for organizations to trust and use data effectively is the foundation for innovation, growth, and competitiveness.
A high-quality data ecosystem drives change and innovation, enabling organizations to identify and seize new opportunities. At the same time, low data quality can lead to inefficiencies and exposure to regulatory and reputational risks.
GLEIF is committed to optimizing LEI data's quality, reliability, and usability. Since 2017, it has published dedicated monthly reports to transparently demonstrate the overall level of data quality achieved in the Global LEI System.
To aid broader industry understanding and awareness of GLEIF’s data quality initiatives, this new blog series explores key metrics included within the reports.
This month’s blog examines Bulk Challenges.
As we have explored previously, the Challenge LEI Data service was introduced to reinforce the accuracy of the Global LEI System by enabling any user of LEI data to challenge the accuracy or completeness of any LEI record at any time. This capability helps maintain high confidence in the Global LEI System, safeguarding businesses, regulators, and financial institutions against operational and regulatory risks.
Scaling trust: Bulk Challenges as the engine of LEI reliability
As LEI challenge volumes rise, GLEIF’s Bulk Challenge feature is emerging as a vital tool in safeguarding the integrity of the LEI ecosystem and credibility of LEI records.
A Bulk Challenge is a streamlined feature designed to help users efficiently manage and process numerous LEI challenges under a unified framework. Instead of submitting and handling each challenge individually, users can group several related challenges under a common title and description.
This makes the overall process faster, more organized, and significantly easier to track. By identifying discrepancies and resolving them quickly and consistently, the accuracy and global reliability of LEI reference data are preserved.
Understanding the benefits of Bulk Challenges
Efficiency at scale: Users can submit and manage numerous LEI challenges in a single operation, saving time and reducing manual workload.
Centralized tracking: Grouped challenges allow for easier tracking and resolution through a unified interface.
API integration: The submission and follow-up of challenges can be automated through seamless API connectivity, making it ideal for users who need to regularly handle high volumes of LEI data records.
Consistency across submissions: Using one title and description for related challenges ensures standardized documentation.
Improved user experience: Enables a more intuitive and less burdensome way to manage large sets of data corrections or disputes.
How the Bulk Challenge process works
Initialize a bulk – To begin, the user creates a new Bulk Challenge by providing a title and a brief description that summarizes the purpose or scope of the grouped challenges. Upon successful submission of this information, the system generates a unique bulk identifier, which is used to associate all subsequent challenge entries.
Attach individual challenges – Using the generated bulk identifier, users can now start linking individual challenges to the bulk. Each challenge includes:
The specific data fields being challenged;
The corresponding LEI(s), and;
Any supporting evidence or documentation to justify the modification.
Seal the bulk – Once all the individual challenges have been added, the bulk must be sealed. Sealing indicates that the user has finalized the entries and is ready to submit the entire batch for review. No further modifications can be made to the bulk after this step.
Submit and track – Once submitted, the system then automatically routes each individual challenge to the respective LEI issuer responsible for the relevant LEI(s). Once the LEI managing organization validates the information, the LEI records are updated accordingly.
It is important to note that GLEIF only enables the use of Bulk Challenges following a careful review of the requester’s profile and intended application of the feature. To safeguard the performance, reliability, and stability of the Global LEI System, GLEIF retains the discretion to control access, ensuring that LEI managing organizations are not overwhelmed.
How Bulk Challenges combine with automation and targeted action
Combining the benefits of the Bulk Challenge feature with the power of both automation and targeted action further enhances its efficacy, bolstering the accuracy, timeliness, and reliability of the LEI ecosystem.
Data feeds represent a proactive form of Bulk Challenge implemented by GLEIF to strengthen the integrity of the Global LEI System. GLEIF collects external data related to existing LEI records, automatically preprocesses this information, and generates bulk challenges for predefined cases at regular intervals. These external data streams are integrated to capture critical legal entity events, such as corporate actions or structural changes, that may necessitate updates or corrections to existing LEI records.
Since January 2024, tens of thousands of automated data feeds have triggered updates, proving the system's ability to detect and act on high-impact changes in near real time. Operating independently of the regular annual renewal cycle for LEI reference data, data feeds have triggered an impressive 51,182 verification and update requests between January 1, 2024, and the end of April 2025. Notably, of the ones that have been processed and closed, 62,42% resulted in updates to the LEI reference data. This continuous flow of additional supplemental information highlights the system’s advancing capability to detect and process high-impact changes at scale, reinforcing trust in the data that powers global financial transparency.
Additionally, targeted bulk challenges can be used to address specific issues. For example, comparing names and address information between the LEI records and Companies House data in Great Britain has triggered 1,718 challenges as part of GLEIF’s Data Quality Initiatives. To date, 1,717 of these challenges have been closed, with 631 processed during April 2025 alone. Of these, 1,598 updates – representing an acceptance rate of 93% - were accepted, demonstrating the positive impact of our focused analysis in bridging gaps between official registry sources and LEI reference data.
Transforming data into opportunities
The ability to resolve large volumes of inquiries quickly and consistently is increasingly vital.
GLEIF’s Challenge Management Facility addresses this need by providing a robust and powerful tool for verifying concerns related to LEI reference data or inconsistencies during comparison with internal data, whether submitted individually or as part of a bulk process.
Proactively addressing Bulk Challenges at scale safeguards the integrity of the Global LEI System and enables faster, more informed decision-making. In doing so, they empower global markets with accurate, actionable data, reinforcing financial transparency and trust worldwide.
All interested parties are encouraged to contact GLEIF for more information on accessing the Bulk Challenge functionality.
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Zornitsa Manolova leads the Data Quality Management and Data Science team at the Global Legal Entity Identifier Foundation (GLEIF). Since April 2018, she is responsible for enhancing and improving the established data quality and data governance framework by introducing innovative data analytics approaches. Previously, Zornitsa managed forensic data analytics projects on international financial investigations at PwC Forensics. She holds a German Diploma in Computer Sciences with a focus on Machine Learning from the Philipps University in Marburg.
