  Existing system dealing with online complaint provides a final decision
without explanations. We propose to analyse the complaint text of internet
fraud in a fine-grained manner. Considering the complaint text includes
multiple clauses with various functions, we propose to identify the role of
each clause and classify them into different types of fraud element. We
construct a large labeled dataset originated from a real finance service
platform. We build an element identification model on top of BERT and propose
additional two modules to utilize the context of complaint text for better
element label classification, namely, global context encoder and label refiner.
Experimental results show the effectiveness of our model.
