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.
About the Client
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, they required a solution that could enhance their existing system’s ability to detect high-risk anomalies with greater precision and scalability.
Client’s Challenge
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 the following:
- Missed Risks/False Negatives: The existing rule-based system often marked high-risk transactions as “No Risk,” creating vulnerabilities in the import/export process.
- Undetected Unusual Transactions: The system struggled to identify unexpected imports and exports, which could indicate fraud or non-compliance.
- Limited Analysis Capabilities: Traditional methods couldn’t handle the complexity of identifying nuanced patterns or hidden anomalies in vast transaction datasets.
- 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.
The Solution
INTECH developed an Integrated Customs System (ICS) that combined statistical and model-based anomaly detection techniques to address the client’s challenges.
Key Features
- Deep Anomaly Detection: Combined both, value-based and statistical-based approaches for deep analysis of suspicious transactions.
- Multi-Model Integration: Integrated various machine learning algorithms together to detect the most critical import and export transactions.
- Attribute Analysis: Identified specific variables that are most responsible for anomalies, including particular ports, particular product codes, or certain business codes.
Tools and Technologies Used
- Python: For core development machine learning, data analytics, data visualization, and programing applications.
- Streamlit: To create a user-friendly web interface for model deployment and user interaction.
Implementation Approach
Step 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.
Step 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.
Step3: 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.
Step 4: Testing and Validation
User feedback was incorporated to refine the models, ensuring accurate anomaly detection and reducing false positives.
Key Outcomes
The implementation of ICS yielded measurable improvements in customs security operations:
- Successfully flagged 0.6–0.8% out of 0.5 million transactions as high-risk anomalies.
- Identified 0.5–0.6% of samples as less critical yet suspicious.
- Reduced False Positives: Improved accuracy by lowering incorrect flags, ensuring legitimate transactions proceeded without disruption.
- Pattern Analysis: Highlighted key variables, such as specific ports or product codes, responsible for anomalies.
Business Impact
Our AI-powered ICS transformed the client’s customs operations by:
- Strengthening Risk Detection: Identifying anomalies overlooked by traditional systems, enhancing security measures.
- Improving Efficiency: Reducing manual intervention, saving time and resources.
- Enabling Proactive Monitoring: Detecting unexpected imports/exports and outliers early to prevent potential risks.
- Scalability: Ensuring the system could handle increasing transaction volumes without performance degradation.
- Assisting Human Decision-Making: Empowering users with accurate, data-driven insights to make informed decisions quickly.
The system not only improved the client’s operational efficiency but also established a scalable framework for ongoing security needs.