  Rapid growth of modern technologies such as internet and mobile computing are
bringing dramatically increased e-commerce payments, as well as the explosion
in transaction fraud. Meanwhile, fraudsters are continually refining their
tricks, making rule-based fraud detection systems difficult to handle the
ever-changing fraud patterns. Many data mining and artificial intelligence
methods have been proposed for identifying small anomalies in large transaction
data sets, increasing detecting efficiency to some extent. Nevertheless, there
is always a contradiction that most methods are irrelevant to transaction
sequence, yet sequence-related methods usually cannot learn information at
single-transaction level well. In this paper, a new "within->between->within"
sandwich-structured sequence learning architecture has been proposed by
stacking an ensemble method, a deep sequential learning method and another
top-layer ensemble classifier in proper order. Moreover, attention mechanism
has also been introduced in to further improve performance. Models in this
structure have been manifested to be very efficient in scenarios like fraud
detection, where the information sequence is made up of vectors with complex
interconnected features.
