NNEnsLeG: A novel approach for e-commerce payment fraud detection using ensemble learning and neural networks

Abstract

The proliferation of fraud in online shopping has accompanied the development of e-commerce, leading to substantial economic losses, and affecting consumer trust in online shopping. However, few studies have focused on fraud detection in e-commerce due to its diversity and dynamism. In this work, we conduct a feature set specifically for e-commerce payment fraud, around transactions, user behavior, and account relevance. We propose a novel comprehensive model called Neural Network Based Ensemble Learning with Generation (NNEnsLeG) for fraud detection. In this model, ensemble learning, data generation, and parameter-passing are designed to cope with extreme data imbalance, overfitting, and simulating the dynamics of fraud patterns. We evaluate the model performance in e-commerce payment fraud detection with >310,000 pieces of e-commerce account data. Then we verify the effectiveness of the model design and feature engineering through ablation experiments, and validate the generalization ability of the model in other payment fraud scenarios. The experimental results show that NNEnsLeG outperforms all the benchmarks and proves the effectiveness of generative data and parameter-passing design, presenting the practical application of the NNEnsLeG model in e-commerce payment fraud detection.