  Fraud detection is to identify, monitor, and prevent potentially fraudulent
activities from complex data. The recent development and success in AI,
especially machine learning, provides a new data-driven way to deal with fraud.
From a methodological point of view, machine learning based fraud detection can
be divided into two categories, i.e., conventional methods (decision tree,
boosting...) and deep learning, both of which have significant limitations in
terms of the lack of representation learning ability for the former and
interpretability for the latter. Furthermore, due to the rarity of detected
fraud cases, the associated data is usually imbalanced, which seriously
degrades the performance of classification algorithms. In this paper, we
propose deep boosting decision trees (DBDT), a novel approach for fraud
detection based on gradient boosting and neural networks. In order to combine
the advantages of both conventional methods and deep learning, we first
construct soft decision tree (SDT), a decision tree structured model with
neural networks as its nodes, and then ensemble SDTs using the idea of gradient
boosting. In this way we embed neural networks into gradient boosting to
improve its representation learning capability and meanwhile maintain the
interpretability. Furthermore, aiming at the rarity of detected fraud cases, in
the model training phase we propose a compositional AUC maximization approach
to deal with data imbalances at algorithm level. Extensive experiments on
several real-life fraud detection datasets show that DBDT can significantly
improve the performance and meanwhile maintain good interpretability. Our code
is available at https://github.com/freshmanXB/DBDT.
