Multi-task CNN Behavioral Embedding Model For Transaction Fraud Detection
This work addresses fraud detection for e-commerce platforms, representing an incremental improvement with domain-specific applications.
The paper tackles transaction fraud detection in e-commerce by introducing a multitask CNN behavioral embedding model that balances modeling capabilities and efficiency while incorporating domain knowledge. The model achieves enhanced performance on downstream transaction models and shows comparable competitiveness with the Transformer Time Series model on real-world data.
The burgeoning e-Commerce sector requires advanced solutions for the detection of transaction fraud. With an increasing risk of financial information theft and account takeovers, deep learning methods have become integral to the embedding of behavior sequence data in fraud detection. However, these methods often struggle to balance modeling capabilities and efficiency and incorporate domain knowledge. To address these issues, we introduce the multitask CNN behavioral Embedding Model for Transaction Fraud Detection. Our contributions include 1) introducing a single-layer CNN design featuring multirange kernels which outperform LSTM and Transformer models in terms of scalability and domain-focused inductive bias, and 2) the integration of positional encoding with CNN to introduce sequence-order signals enhancing overall performance, and 3) implementing multitask learning with randomly assigned label weights, thus removing the need for manual tuning. Testing on real-world data reveals our model's enhanced performance of downstream transaction models and comparable competitiveness with the Transformer Time Series (TST) model.