Enhancing Customs Security with AI-Powered Integrated System

INTECH developed an AI-powered Integrated Customs System (ICS) designed to work alongside existing Cargoes Customs platforms. The ICS helps identify suspicious transactions that rule-based algorithms have marked as “No Risk,” particularly focusing on unexpected imports and exports. Our solution used both statistical and machine learning (ML) models to improve the accuracy of anomaly detection, reduce false alarms, and make customs operations more efficient

The client is a renowned logistics and cargo management organization that operates globally. Their Cargoes Customs platform processes massive volumes of import/export data daily. To maintain security and compliance, the client needed a solution that could enhance their ability to detect high-risk anomalies with greater precision and scalability.

The global logistics leader needed to manage complex customs operations to detect suspicious transactions accurately and reduce manual intervention while handling growing trade volumes.

Their key challenges included:

1. Missed Risks/False Negatives::

The existing rule-based system often marked high-risk transactions as “No Risk,” creating vulnerabilities in the import/export process

2. Undetected Unusual Transactions::

The system struggled to identify unexpected imports and exports, which could indicate fraud or non-compliance.

3. Limited Analysis Capabilities::

Traditional methods couldn’t handle the complexity of identifying nuanced patterns or hidden anomalies in vast transaction datasets.

4. Heavy Manual Review Load:

With numerous flagged transactions requiring human verification, the team faced significant resource strain, slowing down the review process.

These challenges highlighted the need for an advanced solution that could enhance security measures, reduce false positives, and streamline operations.

INTECH developed an Integrated Customs System (ICS) that combined statistical and model-based anomaly detection techniques to address the client’s challenges.

1. Value and Statistical-Based Anomaly Detection

The first step in the implementation process involved using statistical methods to detect anomalies in the transaction data. 

We used InterQuartile Range (IQR) and business variable stats, to detect if values were much smaller or larger than a certain range and flagged them as anomalies.

2. Model-Based Anomaly Detection:

Next, we focused on incorporating machine learning models for anomaly detection. 

We used supervised and unsupervised machine learning techniques to predict the Assessment of Risk (AOR) status of each transaction. 

Different machine learning algorithms were implemented like Value based, Statistical model, Isolation model and XGBoost model – to detect the most suspicious and unexpected transactions. 

The models analyzed patterns to predict high-risk transactions and were compared with the rule-based system for more precise anomaly detection.

3. Model Integration and Aggregation:

The individual models were integrated into a unified system using a voting mechanism to aggregate results and classify anomalies by severity. 

This approach enhanced accuracy in detecting critical and less critical transactions, with a feedback loop enabling continuous improvement through user input.

4. Testing and Validation:

User feedback was incorporated to refine the models, ensuring accurate anomaly detection and reducing false positives.