Custom Fraud

Customs fraud detection is a critical area of research focusing on improving the accuracy and efficiency of identifying fraudulent import declarations. Current research emphasizes the use of machine learning, particularly deep learning models like LSTM networks and graph neural networks, often incorporating multimodal data (images and text) to enhance prediction accuracy and handle data scarcity. These advancements aim to automate fraud detection, improve risk assessment, and ultimately strengthen international trade security and revenue collection by leveraging explainable AI and knowledge sharing across customs administrations.