Sayari: Tackling Commercial Risks in Supply Chains

Colby Potter,  is a Senior Solutions Manager for Sayari, overseeing a team of analysts that work with Sayari customers, including banks and multinationals, to help uncover risk in their supply chains and counterparty networks.

Sayari is a counterparty and risk identification solution that provides analysts and investigators with deep visibility into corporate and trade networks supply chain risks such as forced labour and sanctions violations for governments and corporations.

Sayari has some of the most comprehensive data on global sanctions, watchlists, illicit activity import/export compliance and controls. It counts some of the largest brands and companies in the aerospace, fashion, automotive and semiconductor industries among its customers. Additionally, its products provide key intelligence to government agencies, including the Department of Energy (DOE), US Customs and Border Protection and the US Department of the Treasury.

Colby spoke to Procurement Magazine on why he believes that instead of banning goods based on category or country, he suggests governments should enhance data collection on shipments to enable risk-based assessments of their contents, owners and origins.

Why do you believe regulators are tackling forced labour issues incorrectly and what should happen instead?

Regulators have introduced several bills to Congress that seek to change the de minimis rule in slightly different ways.

One approach would prohibit certain categories of goods, such as cotton or polysilicon, that are deemed 'import sensitive,' are subject to specific tariff restrictions, or originate from high-risk economies.

Another approach focuses on imports from jurisdictions that are high-risk under the UFLPA framework or suspected of higher risk for illicit activities.

While both approaches address forced labour issues, regulators must recognise that data is the most important piece of the puzzle.

An ideal amendment to the de minimis rule would require a more robust collection of information on shipments that fall under de minimis. Improved classification of the contents of these shipments, their origins and their senders is vital for effective risk-based screening for forced labour risk.

Additionally, de minimis represents one way entities engaged in forced labour can avoid notice, but it’s not the only one. Simply addressing the de minimis loophole in forced labour screening is only one facet of a complicated issue.

The risk is increasingly opaque, such as when shipments tied to forced labour are re-routed via third countries or subsidiaries. Responding to one challenge requires addressing these other challenges if an approach to countering forced labour is to be as comprehensive as possible.

What are the specific challenges companies are facing when trying to combat forced labour?

It is challenging for companies to identify risky de minimis shipments or related shippers under the current threshold. Risky shipments can sometimes occur several tiers upstream of a direct supplier. Identifying de minimis shipments coming into the US is not enough. Enterprises are increasingly expected to have visibility into upstream risks.

The standard approach of using questionnaires and social audits only goes so far. Companies need access to reliable data and platforms to gather, process and interrogate a deluge of information in order to gain key insights into where they may be exposed to risk.

What compliance rules were introduced to help combat this and how are companies working towards it?

There’s been a concerted push by many national governments to enforce anti-forced labour measures. Over the past few years, we’ve seen highly impactful laws like the NFLPA, the EU’s Corporate Sustainability Due Diligence Directive and Canada’s S-211 bill enacted or agreed to. This is in addition to pre-existing anti-forced labour laws like the UK’s Modern Slavery Act.

Corporations now have a growing obligation to comply with these regulations. However, entities engaging in forced labour are increasingly trying to obscure their practices, placing increased responsibility on corporations to monitor not only obvious forced labour risks but also those hidden behind multi-layered trade and ownership relationships.

We've seen companies adopt and try to integrate several tools to help draw connections among upstream shipments and suppliers to save money and time, but rarely does this solve the still pervasive problem of interoperability in supply chain management, where disparate systems often fail to communicate effectively – leading to inefficiencies and gaps in data.

It's no easy feat. These shipments represent millions of transactions and millions of dollars worth of trade. Being able to account for any significant portion of it requires technological solutions to analyse and screen at scale.

How can companies use AI to close compliance gaps?

While AI holds much promise in closing compliance gaps in the supply chain, it can only help when coupled with the right approach. It can easily go wrong if there’s an overreliance on AI or if it’s used to fill in gaps of missing information without oversight, as it can easily lead to inaccurate conclusions. Companies need to keep in mind two things: 

Companies should use AI to automate and accelerate data collection, rather than relying on it to infer or predict missing information. Using AI in a predictive capacity can lead to inaccurate conclusions. The better approach is to use AI where it can be relied upon, to help classify and validate actual data, resulting in more immediate, verifiable insights. 

Remember to pair AI with expert analysis and insight: AI should be implemented in tandem with analysts and investigators who bring nuance, background understanding of risk indicators and real-world experience to compliance processes. This will ensure AI systems are not only effective but also transparent and explainable, ensuring that inaccuracies or limitations are addressed before they lead to compliance failures.

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