Balancing Complexity and Technology – How AI Is Transforming Trade Compliance

The intricate nature of trade compliance, coupled with the constant pressure to adapt to changing sanctions and geopolitical risks, often places a heavy burden on logistics professionals and businesses that are trading globally. Since the stakes are so high, companies are looking for any help they can get to manage the growing complexity that is related to the task at hand. In fact, a Descartes study revealed that 74% of logistics leaders are prioritising technology for growth amid global trade challenges. What is more, with the rise of artificial intelligence (AI), one of the many questions on people’s minds is, “Is now the time for trade compliance professionals to leverage AI?” Jackson Wood, Director of Industry Strategy, Global Trade Intelligence, Descartes, shares his insights, explaining how AI can transform trade compliance.

The Complexity of Trade Compliance and Denied Party Screening AI

The scope of trade compliance has broadened significantly beyond sanctions, navigating tariffs, or ensuring ethical sourcing. Today, denied party screening AI is being considered by trade compliance professionals too. This addresses a multitude of regulations which incorporate environmental standards, carbon footprint, conflict minerals, microchips, forced labour, trade and data privacy laws. 

As global trade continues to become more interconnected and regulations more stringent, the responsibilities of compliance professionals are ever-increasing, placing more and more pressure on people’s shoulders.  Beyond ensuring legal adherence, there is a growing expectation that they play a role in corporate governance, sustainable practices, and collaborative due diligence across departments.

To handle this increased workload, some businesses are looking to AI to automate repetitive tasks to check these situations. For many though, there is a question around whether it really is mature enough for widespread adoption in trade compliance teams right now?

Challenges for AI Automation in Denied Party Screening

Although AI has come along in leaps and bounds in the past few years, there are still a number of challenges facing automated AI systems. AI automation currently struggles with some of the complexities and nuances related to managing trade compliance.

This is because trade regulations can be highly intricate, with many grey areas that require human interpretation. AI also operates on algorithms and data, and while it is excellent at processing large volumes of information, it can still introduce biases or make decisions that are not always aligned with ethical or legal standards. Not to mention, AI is only as good as the data it is fed.

In the trade compliance sector, where data may not always be clean, complete, or standardised, AI automation can lead to errors if it is not set up correctly or if it is used incorrectly. That is not to say that it should not be used, but that it needs careful set up, use and monitoring.

Why It Makes Sense to Keep a ‘Human in the Loop’

This is where a ‘Human in the Loop’ provides users a truly balanced solution. Not only does it enable a business to reap the significant benefits of AI, but it also allows for human control over critical decisions. Ultimately, this leads to more reliable outcomes for organisations and their trade compliance teams.

Across the industry, there are many AI-led tools available to use, which can make it difficult to know what to use. With that in mind, the following guiding principles might help trade compliance experts:

1. Automation with Human Oversight

Designed to enhance compliance processes without completely replacing human judgment, it is key to identify an AI tool that truly automates the labour-intensive tasks associated with denied party screening and triaging false positives. This allows compliance teams to focus on higher-level decision-making as they carry out their duties. Furthermore, it provides the best of both worlds: improved efficiency and reduced workload while maintaining human control where it’s most needed.

2. Targeted, Flexible Automation

Customisation is king. There are a range of customisable tools that can be fine-tuned to meet the specific needs of an organisation. Businesses should strive to identify these tools as they can adjust the sensitivity of the AI involved within these tools, to ensure that it works effectively for their specific set of unique compliance requirements. This level of control allows compliance teams to leverage AI without the risk of missing critical information, and to tailor it according to their precise business needs.

3. Improved Accuracy with Less Effort

By isolating low-quality hits and reducing false positives, proven AI tools can help to minimise the risk of human error and fatigue that comes from sifting through irrelevant alerts. This makes the compliance process more accurate without fully handing control over to an automated system.

4. Enhanced Audit Trail and Accountability

A suitable AI tool also provides precise status history tracking. This enhances the transparency and traceability of compliance decisions. Every auto-isolated hit is documented, offering a robust audit trail that is essential for regulatory compliance. This ensures that human decision-making is backed by a reliable denied party screening AI tool without sacrificing accountability.

When is The Right Time to Act?

For some companies, implementing an AI solution can support overwhelmed teams by increasing volumes of trade data and regulatory updates. For other operators, it may be premature if the current process and resources are not exhausted. For many trade compliance teams, adopting a good AI solution is an innovative, low-risk way to start using AI, which can help ease the load and pressure placed on compliance teams. It offers automation where it is needed most while keeping humans in control of critical decisions. 

This is why it is recommended to start by evaluating current challenges. For example, if a business is facing increasing volumes of compliance data, has false positives wasting valuable time, or is struggling to keep up with rapid regulatory changes, then now might be the right time to explore AI, at least in a supportive capacity.

Conclusion

Today, AI is by no means a ‘magic trick’ to be implemented and left to thrive on its own. Implementing it requires a strategic step in a direction towards efficiency gains and growth. When used effectively with a human in the loop, it can enable organisations to navigate the complexity of trade compliance with greater confidence, speed, and accuracy. So, with the AI transformation already underway, the question isn’t whether AI will play a role in trade compliance, it’s how and when a business will take the first step?