Fine-Grained Element Identification in Complaint Text of Internet Fraud
This work addresses the need for interpretable fraud detection systems in finance services, though it is incremental as it builds on existing BERT models with added modules.
The paper tackles the problem of providing explanations for online fraud complaint decisions by analyzing complaint text in a fine-grained manner to identify the role of each clause and classify them into fraud element types, achieving effective results as shown in experiments.
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.