LEI can bring a true breakthrough in the way US Federal Government identifies businesses.

In a fresh new report, published by the Data Foundation and the Global Legal Entity Identifier Foundation (GLEIF), we see a very deep exploration of the needs by the U.S. federal agencies to adopt a universal entity identification system.

The report reveals that the U.S. federal government uses about 50 distinct identification schemes – and that all of them are separate and incompatible with each other. What is even worse, many of them depend on proprietary, for-profit initiatives, creating unwanted dependencies and incurring non-negligible costs.

This situation creates a significant challenge for many of the federal agencies, because the existing systems “lack clarity, data quality, or consistent data models”.

The report shows how the adoption of the LEI system could help the agencies, and bring both a significant reduction of cost of business identification and improve the quality and consistency of the related data. As the LEI system is universal, different agencies could easily share their data using the common identifier, i.e. the LEI code. The system is also open sourced, what dramatically reduces the cost of its use and the maintenance of its data inside any given government agency.

The report shows a path to the adoption, illustrating how the conversion to a comprehensive identification system could happen, and illustrates the process by the mapping between the Business Identifier Code (BIC) – used by SWIFT and the LEI system (see our LEI.INFO implementation of this mapping for a typical bank, e.g. at: https://lei.info/724500AH42V5X8BCPE49)

The conclusion of the report is that there exist a solution to all of the current identification problems which can “bridge gaps in understanding across regulators, across government, across sectors, and across the world.” That solution is the adoption of the LEI system.

We, at LEI.INFO are ready to help government institutions on that path. Many of the solutions we have implemented for our system (particularly including intelligent, self-descriptive data models) can make the conversion and its later use much easier, more secure and finally – even more cost effective.

Author: Cheron Hampton-Bates