Financial fraud prediction leveraging knowledge graphs and multimodal features
Year: 2025
Type: article
Abstract: Aiming at the problem that existing financial fraud prediction methods have single feature extraction and do not fully utilise multimodal financial features, this paper first constructs a knowledge graph based on multimodal financial data, and uses BERT and ResNet to extract features from text entities and image entities respectively. A bidirectional cross-modal attention mechanism is designed, and a bidirectional text and image element matching method is proposed. The consistency of all element pairs is comprehensively used to represent the global consistency features of text and images. Finally, a hybrid fusion method is designed to fuse text features, image features, and text-image consistency features. The final prediction result is obtained through a fully connected layer. Experimental results show that the proposed model has a prediction accuracy of 92.4%, which is better than the comparative methods and can effectively improve the accuracy of financial fraud prediction.
Author: Jiangyan Cheng
Institution: Fujian Polytechnic of Information Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414211643
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