A Multimodal Deep Neural Network-based Financial Fraud Detection Model Via Collaborative Awareness of Semantic Analysis and Behavioral Modeling

被引:0
|
作者
He, Dingzhou [1 ]
机构
[1] Southwest Univ Polit Sci & Law, Coll Natl Secur, Chongqing, Peoples R China
关键词
Multimodal deep learning; fraud detection; sentiment analysis; behavioral modeling;
D O I
10.1142/S0218126625500549
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
The monitoring and early warning of financial risks have become a crucial link in maintaining market stability and safeguarding the rights and interests of investors. Traditional financial risk monitoring methods often rely on a single data source or analysis model, making it challenging to comprehensively and accurately capture risk signals. Therefore, this paper proposes a novel financial risk monitoring model based on multimodal neural networks, which innovatively integrates multiple data sources, such as vision, language and audio, and utilizes their inherent correlations to enhance the accuracy of risk identification. First, by employing the Bidirectional Long Short-Term Memory Network (BiLSTM) structure and incorporating the self-attention mechanism, the semantic information of financial texts is deeply analyzed through the calculation of dynamic weight coefficients. Additionally, Option-based Hierarchical Reinforcement Learning (OHRL) is utilized to accurately model the behavior of market participants, capturing nuanced changes in their decision-making process. By integrating these two types of information, a comprehensive BiLSTM-OHRL model is formulated to evaluate the risk status of financial markets in a more comprehensive and accurate manner. The results demonstrate that the model performs impressively in financial risk monitoring, accurately capturing the emotional and behavioral characteristics of market participants, thereby enhancing the comprehensiveness and predictive capability of the monitoring model. It provides robust technical support for the stable operation of the financial market.
引用
收藏
页数:25
相关论文
共 50 条
  • [41] An Intelligent Error Detection Model for Machine Translation Using Composite Neural Network-Based Semantic Perception
    Wu, Yaoxi
    Liang, Qiao
    IEEE ACCESS, 2024, 12 : 113490 - 113501
  • [42] Integrating articulatory data in deep neural network-based acoustic modeling
    Badino, Leonardo
    Canevari, Claudia
    Fadiga, Luciano
    Metta, Giorgio
    COMPUTER SPEECH AND LANGUAGE, 2016, 36 : 173 - 195
  • [43] Uncertainty quantification of deep neural network-based turbulence model for reactor transient analysis
    Liu, Yang
    Hu, Rui
    Balaprakash, Prasanna
    Proceedings of the 2021 ASME Verification and Validation Symposium, VVS 2021, 2021,
  • [44] Deep Neural Network-Based Simulation of Sel'kov Model in Glycolysis: A Comprehensive Analysis
    Ul Rahman, Jamshaid
    Danish, Sana
    Lu, Dianchen
    MATHEMATICS, 2023, 11 (14)
  • [45] UNCERTAINTY QUANTIFICATION OF DEEP NEURAL NETWORK-BASED TURBULENCE MODEL FOR REACTOR TRANSIENT ANALYSIS
    Liu, Yang
    Hu, Rui
    Balaprakash, Prasanna
    PROCEEDINGS OF THE 2021 ASME VERIFICATION AND VALIDATION SYMPOSIUM (VVS2021), 2021,
  • [46] Global Surrogate Modeling by Neural Network-Based Model Uncertainty
    Leifsson, Leifur
    Nagawkar, Jethro
    Barnet, Laurel
    Bryden, Kenneth
    Koziel, Slawomir
    Pietrenko-Dabrowska, Anna
    COMPUTATIONAL SCIENCE - ICCS 2022, PT III, 2022, 13352 : 425 - 434
  • [47] Recurrent Neural Network-Based Multimodal Deep Learning for Estimating Missing Values in Healthcare
    Kim, Joo-Chang
    Chung, Kyungyong
    APPLIED SCIENCES-BASEL, 2022, 12 (15):
  • [48] Deep Learning Neural Network-Based Weibo Sentiment Analysis
    Wang, Yiming
    Fang, Chun
    PROCEEDINGS OF 2024 4TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND INTELLIGENT COMPUTING, BIC 2024, 2024, : 7 - 11
  • [49] A Model Based on Convolutional Neural Network for Online Transaction Fraud Detection
    Zhang, Zhaohui
    Zhou, Xinxin
    Zhang, Xiaobo
    Wang, Lizhi
    Wang, Pengwei
    SECURITY AND COMMUNICATION NETWORKS, 2018,
  • [50] A Survey: Neural Network-Based Deep Learning for Acoustic Event Detection
    Xia, Xianjun
    Togneri, Roberto
    Sohel, Ferdous
    Zhao, Yuanjun
    Huang, Defeng
    CIRCUITS SYSTEMS AND SIGNAL PROCESSING, 2019, 38 (08) : 3433 - 3453