Fine-Grained Element Identification in Complaint Text of Internet Fraud

TL;DR

This paper introduces a fine-grained method for analyzing internet fraud complaints by identifying and classifying clauses into fraud elements, using a BERT-based model with context-aware modules, to improve explanation quality.

Contribution

It presents a novel approach with a large labeled dataset and enhanced BERT model incorporating global context and label refinement for detailed complaint analysis.

Findings

Model outperforms baseline methods in accuracy

Effective utilization of context improves classification

Large real-world dataset supports practical application

Abstract

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.