Enhancing Fraud Detection with Multimodal Learning

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This presentation explores a cutting-edge solution for detecting fraudulent last-mile deliveries, a significant issue within the e-commerce sector. We address challenges like unreliable GPS data and limitations of current methods, such as TextCNN, which are hampered by single modality reliance. Our proposed MFDD Framework leverages multi-modal data integration, utilizing advanced techniques like text embeddings, Attention+CNN for pattern analysis, and cross-modal fusion. This approach aims to...