How Artificial Intelligence Simplifies Customs Processing

The correct customs classification of products is complex and prone to errors. Outdated definitions, confusing legal sources, and missing product information lead to high manual effort and uncertainty in many companies. But there are solutions: New technologies based on artificial intelligence – particularly Deep Learning – promise relief in this area.

In this article, we explore how AI is transforming customs classification, which previous approaches are reaching their limits, and why humans are not being replaced in the process.

The Customs Tariff: Challenges for International Online Retailers

We have summarized the practical hurdles that often arise in customs classification:

• Outdated formulations in the definitions of the customs tariff can cause problems, especially when there is little knowledge in the company about what the definition means in practice.

  
  

• Countless cross-references within the customs tariff: Each definition contains exceptions and exceptions to the exceptions. Notes such as allocation notes or heading wording make it particularly difficult for laypersons to determine the correct customs tariff number.

  
  

• Different interpretations should not occur, but practice shows that in some cases customs classifications in different countries or regions vary.

  
  

• The legal development of the nomenclature often does not keep pace with the rapid product development and introduction of new products.

  
  

• The entirety of legal sources is unclear and partly not accessible in different languages.

  
  

• The large volume of data records that must be correctly classified under time pressure overwhelms many companies.

  
  

• Missing product information and/or poor product descriptions make product identification very cumbersome, sometimes even impossible.

  
  

How Can Artificial Intelligence Help? Artificial Intelligence, Machine Learning, Deep Learning? What is What?

These terms are heard and read in various contexts. But how do the approaches differ? And where are there overlaps?

Artificial Intelligence (AI): Is a broad term that refers to the ability of machines to demonstrate human-like intelligence in a variety of tasks. This could already be a PC.

Machine Learning (ML): Is a specific method of AI that enables machines to automatically learn from experiences and data without explicit programming. There are different types of ML: for example, a distinction is made between supervised and unsupervised learning.

Deep Learning (DL): Is a subfield of ML. It is based on the use of neural networks to recognize complex patterns in data. Unlike ML, the AI is not instructed on which data points to focus on, i.e., which features are of interest. The machine is expected to learn from very large data sets which features to concentrate on best.

Compared to conventional ML methods, DL can achieve better results in processing large and complex data sets. With each step, the explicit input from the programmer decreases and the importance of the data, with which the machine learns, increases. The learning process becomes independent of the person who trains and programs the AI.

Previous Solutions for Customs Classification of Products

The existing solutions are based on well-maintained core data. All characteristics of a product are individually processed and stored. Based on this data, one of two approaches is often pursued:

1. Fuzzy Match Solution

"Matching" is a procedure whereby the characteristics of a product are compared with the characteristics of all existing and classified products. Products that exhibit a high similarity are presented to the customs classification expert or classified automatically. This method is particularly useful when a company has a well-maintained product database. Companies that specialize in specific product groups and offer many similar products can thus accelerate customs classification.

However, this method also reaches its limits. For example, if a company works with foreign product information, such as when importing products, the matching procedure is difficult to apply. Even in the classification of new or different products, a matching system is rarely helpful. There is a risk that incorrect customs classifications from existing data are transferred to new products.

2. Decision Tree Logic

Rule-based systems are intended to translate the complex logic of a tariff system into automated decisions. The decisions can be made by humans or based on existing product data to determine an appropriate tariff number. However, extracting these decisions is very labor-intensive and requires a careful analysis of the product groups.

Rule-based systems are prone to problems. The systems are based on many assumptions and can quickly fail under the complexity of the problem. For example, it may be necessary to narrow down to a few subchapters to simplify the logic of the tariff system. However, this can lead to problems, such as when relevant tariff numbers are not considered. In addition, the maintenance, adjustment, and workload is particularly high and poses a great challenge in complex tariff systems.

3. Latest Technology: Deep Learning – Advantages in Classification

Compared to Fuzzy Matching and Decision Tree solutions, DL technology offers a complete and unrestricted possibility for automatic customs classification of products. Through the use of proprietary AI models and high-quality data sources, the AI can be trained such that it independently learns a function from the product description as well as other input parameters to the tariff number. The AI is modeled through a massive neural network that distills the knowledge from historical decisions from proprietary data sources into the neural network during the training process.

In contrast to rote memorization of data, the AI learns to generalize the underlying concepts and rules and to understand connections. As a result, the AI achieves higher accuracy in product classification. However, implementing DL technology requires extensive preparation and training of the system.

Role of the AI, Role of Humans

The collaboration of AI software and human intelligence generally leads to higher productivity and lower error rates. The AI can detect errors that humans might overlook. Conversely, humans possess knowledge that the AI lacks. This "four-eyes principle" – or "Human in the Loop" – is particularly important in ambiguous or difficult classification cases, such as when information is missing or decisions are complex.

Furthermore, partial automation can be sought – especially in simple or repetitive cases. In these cases, users can review the results, monitor processes, and handle classification tasks. At the same time, users receive all essential functions and tools they expect from a customs classification expert, such as the current tariff schedule, notes, explanatory notes, court decisions, and binding customs tariff information. Despite the AI system, the user retains full control over the classification process.

traide Support

Manual classification takes time, ties up skilled personnel, and is prone to errors with far-reaching consequences – from incorrect duty collection to issues in export control.

traide AI takes a new approach: Our Deep Learning-based solution combines modern AI with practical classification experience.

It identifies relevant product features even from incomplete or unstructured data sources (e.g., webshop texts or data sheets) and automatically assigns them to the appropriate customs tariff number. At the same time, the system checks existing core data and provides automatic feedback for incorrect classifications.

Through the "Human-in-the-Loop" principle, humans remain consistently involved—especially in complex cases. The result: more security, fewer errors, maximum scalability.