Uncovering Import Document Fraud: Leveraging the Deep Learning Approach

The scrutiny, identification, and verification of handwritten signatures on Certificates of Origin (C/O) is a critical task for customs authorities in preventing trade fraud. This task remains a significant challenge due to limitations in manpower and the need for manual verification amidst a vast volume of documents. Deep learning (DL) algorithms offer a valuable solution to address this issue. This paper deploys a Siamese Neural Network (SNN) model to assist customs officials in identifying and verifying handwritten signatures on C/O. The results demonstrate the superior performance of the SNN model over conventional Convolutional Neural Network (CNN) models and Machine Learning (ML) models, with accuracy, precision, recall, F1-score, and AUC values of 0.943, 0.912, 0.899, 0.905, and 0.919, respectively. The paper also provides in-depth analyses of omission cases and suggests applications of the model to support the work of customs officials.