Trust, Automation, and the Growing Role of the LEI: Key Takeaways from Sibos 2026

Last year, Bloomberg explored how Legal Entity Identifiers (LEIs) could help provide a reliable identity foundation for AI systems by connecting data to verified legal entities. At the time, much of the broader discussion around AI focused on its future potential. At Sibos 2026, that conversation appeared to be moving another step forward, with greater attention on how AI is already being introduced into financial processes.

Sibos 2026 brought together financial institutions, technology providers, global regulators, and market infrastructure participants under the theme Digital Finance for AI-Driven Economies. Across more than 250 sessions, discussions explored how artificial intelligence, payments modernization, digital assets, data standards, and innovative technologies are reshaping global finance.

One theme connected many of these conversations: As financial markets become more automated and interconnected, the need for trusted data, interoperability, and reliable organizational identity continues to grow.

Rather than focusing primarily on the theoretical potential of AI, many discussions centered on its application within processes including compliance, sanctions screening, reconciliation, fraud detection, and payments. Alongside this move toward real-world use came questions about the data, controls, and identity frameworks needed to support it.

That shift from possibility to implementation makes data quality and identity increasingly practical concerns. AI systems depend on reliable and structured information, and the LEI provides a standardized way to identify legal entities across markets and jurisdictions. As automated processes take on activities that previously involved greater manual review, reliably determining which legal entity is associated with a transaction, account, or other financial activity becomes increasingly more important.

AI agents add another dimension. As autonomous systems begin to initiate or support financial activities, questions extend beyond what an AI system knows to who it is acting for and whether it is authorized to act. Understanding the organization behind that activity, and how individuals or digital agents are authorized to act on its behalf, could become a key component of trust in automated financial markets.

Interoperability was another recurring Sibos theme. Financial institutions continue to operate across numerous systems, jurisdictions, and regulatory frameworks, often using different identifiers and data structures. ISO 20022 featured prominently in discussions about improving the structure and consistency of financial information, while other conversations focused on connecting new and existing infrastructures without creating additional fragmentation.

The LEI can support this objective by providing a common organizational identifier that is recognized across systems and borders. It can also be mapped to national and other market identifiers, creating a consistent reference point as entity information moves between systems thereby reducing ambiguity in the exchange of financial data.

These issues become particularly relevant as tokenization and digital finance move toward broader implementation. Sibos discussions increasingly focused not simply on proving that newer technologies work, but on how they can operate at scale alongside established financial infrastructure. Tokenized deposits, digital assets, digital ledgers, and new approaches to cross-border payments all featured prominently.

The underlying technology may change, but trusted identity remains fundamental. Whether activity takes place through traditional infrastructure, a tokenized environment, or an increasingly automated payment process, participants still need to understand the legal entities involved.

Cross-border payments illustrate the same challenge. While domestic payment systems in many markets have become faster and more efficient, moving money internationally continues to involve greater complexity. Sibos discussions emphasized speed, transparency, predictability, and interoperability, all of which benefit from consistent information about the participants exchanging data and value. The evolving FATF Recommendation 16 provides a practical example, with strengthened payment standards recognizing the LEI as an identifier for legal entities in payment information.

Regulation also continues to play a key role in advancing common financial standards. Recent developments provide tangible examples, including the Financial Data Transparency Act (FDTA) in the United States, where federal financial regulators established the LEI as the joint standard for legal entity identification. At the same time, discussions at Sibos highlighted an opportunity for the LEI to provide value beyond regulatory requirements. As markets become more automated, data-driven, and interconnected, broader integration of the LEI into financial workflows can help organizations benefit from standardized identity even where its use is not required.

One of the strongest takeaways from Sibos 2026 was that the discussion around AI and digital finance is increasingly moving from what these technologies could do tomorrow to how they can be implemented responsibly and at scale today. That transition places greater emphasis on reliable data, common standards, interoperability, and confidence in the participants using them.

This progression from theory to real-world application reinforces the role that LEI can continue to play. As financial infrastructure becomes more digital and automated, the LEI provides a consistent foundation for identifying the organizations operating within it.

Bloomberg provides LEI registration and management services while supporting high-quality, reliable entity data. As a GLEIF-accredited issuer, Bloomberg continues to support the development of the Global LEI System and the adoption of standardized organizational identity across financial markets. To learn more about registering or managing LEIs, visit https://lei.bloomberg.com.