摘要：As the rapid development of online transactions, transaction frauds have also emerged seriously. The fraud strategies are characterized by specialization, industrialization, concealment and scenes. Anti-fraud technologies face many challenges under the trend of new situations. In this paper, aiming at sample imbalance and strong concealment of online transactions, we enhance the original deep forest framework to propose a deep forest-based online transaction fraud detection model. Based on the BaggingBalance method we propose, we establish a global sample imbalance processing mechanism to deal with the problem of sample imbalance. In addition, the autoencoder model is introduced into the detection model to enhance the representation learning ability. Via the three-month real online transactions data of a China's bank, the experimental results show that, evaluating by the metric of precision and recall rate, the proposed model has a beyond 10 % improvement compared to the random forest model, and a beyond 5 % improvement compared to the original deep forest model. Download data is not yet available.
其他摘要：As the rapid development of online transactions, transaction frauds have also emerged seriously. The fraud strategies are characterized by specialization, industrialization, concealment and scenes. Anti-fraud technologies face many challenges under the trend of new situations. In this paper, aiming at sample imbalance and strong concealment of online transactions, we enhance the original deep forest framework to propose a deep forest-based online transaction fraud detection model. Based on the BaggingBalance method we propose, we establish a global sample imbalance processing mechanism to deal with the problem of sample imbalance. In addition, the autoencoder model is introduced into the detection model to enhance the representation learning ability. Via the three-month real online transactions data of a China's bank, the experimental results show that, evaluating by the metric of precision and recall rate, the proposed model has a beyond 10 % improvement compared to the random forest model, and a beyond 5 % improvement compared to the original deep forest model.
