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Title: Financial fraud prediction leveraging knowledge graphs and multimodal features

Authors: Jiangyan Cheng¹

Addresses: ¹School of Business Management, Fujian Polytechnic of Information Technology, Fuzhou 350002, China

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.

Keywords: financial fraud prediction; knowledge graph; multimodal features; feature fusion; attention mechanism.

DOI: 10.1504/IJICT.2025.10072942

Received: 21 Jun 2025
Accepted: 15 Jul 2025
Published online: 04 Sep 2025 *