  With the explosive growth of the e-commerce industry, detecting online
transaction fraud in real-world applications has become increasingly important
to the development of e-commerce platforms. The sequential behavior history of
users provides useful information in differentiating fraudulent payments from
regular ones. Recently, some approaches have been proposed to solve this
sequence-based fraud detection problem. However, these methods usually suffer
from two problems: the prediction results are difficult to explain and the
exploitation of the internal information of behaviors is insufficient. To
tackle the above two problems, we propose a Hierarchical Explainable Network
(HEN) to model users' behavior sequences, which could not only improve the
performance of fraud detection but also make the inference process
interpretable. Meanwhile, as e-commerce business expands to new domains, e.g.,
new countries or new markets, one major problem for modeling user behavior in
fraud detection systems is the limitation of data collection, e.g., very few
data/labels available. Thus, in this paper, we further propose a transfer
framework to tackle the cross-domain fraud detection problem, which aims to
transfer knowledge from existing domains (source domains) with enough and
mature data to improve the performance in the new domain (target domain). Our
proposed method is a general transfer framework that could not only be applied
upon HEN but also various existing models in the Embedding & MLP paradigm.
Based on 90 transfer task experiments, we also demonstrate that our transfer
framework could not only contribute to the cross-domain fraud detection task
with HEN, but also be universal and expandable for various existing models.
