by Jelani Harper


In most cases, the correlation between blockchain and artificial intelligence is non-linear. The immutable, distributed ledger system of blockchain is based on trust, whereas questions of fairness, transparency, and trust are central to many contemporary AI applications.

The advanced analytics for which workplace AI is renowned depends upon copious data volumes, optimization by a range of sources, and successful data integrations. According to Dr. Mirek Sopek, Makolab SA CTO and LEI.INFO President, one of the emergent use cases for blockchain yielding considerable enterprise value is its empowerment of platforms serving as “an integrated mechanism starting from LEI that then involves other data sources” ideal for machine learning analytics.

A Legal Entity Identifier (LEI) is a global, uniform way of keeping track of legal entities, companies, and facets of the individuals working for them. Managing LEI on blockchain ensures the necessary trust and certainty optimal for combating financial crimes, streamlining various administrative processes like onboarding, and truly knowing corporate customers, partners, and other businesses.

Leveraging such a platform for integrating data for deep learning and machine learning analytics not only makes these manifestations of AI better, but also “when you have that data integrated, you suddenly get a bigger value because you’re looking at a given entity from a different perspective than just one data source,” Sopek explained. “You get a whole 360 view of a legal entity.”

In verticals like finance and insurance in which there are imperatives to Know Your Customer, the statistical AI analytics of such integrated data on blockchain platforms is invaluable for regulatory compliance, fraud prevention, and solid business relationships.

Integrating Blockchain, LEI and AI

LEI is akin to an Employer Identification Number in the United States or a Value Added Tax number in Europe—identifiers for legal entities such as corporations. However, “LEI is global and has no borders at all for accurate and trustful identification of companies across the globe,” Sopek said. With the Financial Transparency Act before Congress now calling for standard identifiers of financial institutions, and international organizations such as theGlobal Legal Entity Identifier Foundation championing LEI, there’s approximately “1.4 million institutions on the system,” Sopek estimated. One of the benefits of implementing LEI data on blockchain is an expedience of access and assurance between trusted partners—on a worldwide scale—for business purposes.

According to Franz CEO Jans Aasman, another is that “every change that you make with respect to company data” could be stored in blockchain. When reinforced by certain graph database technologies, this aspect of blockchain is ideal for integrating data about those companies to run cognitive computing analytics about them. Sopek referenced “a company that uses Artificial Intelligence to use social media, and dark web, and some other sources, to do their due diligence on people working within organizations” that relies on this method for timely data integrations for analytics. Feeding machine learning models with LEI data integrated on blockchain-based platforms enables comprehensive advanced analytics for understanding customers and partners for regulations and prudent business practices.


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From Know Your Customer to Know Your Business

The conduit from blockchain-based LEI data integration platforms to AI models is of interest to financial organizations in particular, because of the Know Your Customer mandate essential to counteracting money laundering and fraud. However, feeding AI with the aforementioned platforms transforms certain facets of KYC to KYB—Know Your Business. “The system is of course [of value] to people who act in business capacities,” Sopek commented. “But, its primary role is for legal entities.”

Blockchain-based LEI systems make it infinitely more difficult—if not outright impossible—to perpetrate common forms of financial fraud such as operating shell companies and selling fictitious securities or other financial instruments to unsuspecting buyers. In this respect, it’s beneficial for tracking legal entities for virtually any process in which there are “multiple steps where people consolidate these debt securities and sell these other kind of financial instruments, and people lose track of what was the original instrument,” Sopek reflected. LEI delivers these same boons for companies and those associated with them.


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Onboarding and Administration

Blockchain-based LEI platforms are also beneficial for expediting the onboarding process for new companies, partners, suppliers, etc. In this use case it not only decreases time to value for working with those entities, but also reduces the resources and cost associated with the validation required for conducting due diligence about those entities. “If a company is on the LEI system, you automatically have information about it so you have reduced onboarding time,” Sopek said. “Basically you have the link to the organization, it’s legal address, and you also know if the organization is active.” The LEI system continually refreshes its data “making obsolete records almost non-existent or at least marked as such,” Sopek pointed out. Although organizations are still responsible for conducting other facets of onboarding, LEI “gives you the beginning, which is very costly,” Sopek said.

Blockchain and AI

Implementing LEI systems on blockchain platforms not only helps to democratize access to these legal identifiers, but also serves as an expeditious data integration platform for holistic understanding of customers and legal entities. As such, this approach is an excellent means of integrating data for advanced machine learning analytics for understanding customers, calculating risks, and even devising new products or services to offer them. It’s just one of the ways Blockchain can create more effective AI.


Jelani Harper is an editorial consultant servicing the information technology market, specializing in data-driven applications focused on semantic technologies, data governance and analytics.