IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v58y2023ipbs1544612323008309.html
   My bibliography  Save this article

Enhancing financial fraud detection with hierarchical graph attention networks: A study on integrating local and extensive structural information

Author

Listed:
  • Shi, Feifen
  • Zhao, Chuanjun

Abstract

In the highly competitive business environment, financial fraud detection plays a crucial role in safeguarding companies and investors. However, traditional methods face challenges when assessing the complex and multidimensional nature of financial data. Cutting-edge machine learning techniques, particularly the hierarchical graph attention network (HGAT), emerge as a promising approach for financial fraud detection. The proposed approach includes encoding adjacency matrices for capturing local relationships and utilizing multi-head self-attention to propagate structural attributes across multiple layers. Node embeddings are generated by the HGAT model, which integrates both local and extensive structural information through multihead self-attention. Through learning intricate inter-entity relationships, the HGAT model can effectively identify potential financial risks. Experiments performed on a publicly available financial report dataset demonstrate the superior performance of our model compared to the existing methods in detecting financial risks.

Suggested Citation

  • Shi, Feifen & Zhao, Chuanjun, 2023. "Enhancing financial fraud detection with hierarchical graph attention networks: A study on integrating local and extensive structural information," Finance Research Letters, Elsevier, vol. 58(PB).
    • Handle: RePEc:eee:finlet:v:58:y:2023:i:pb:s1544612323008309
    DOI: 10.1016/j.frl.2023.104458
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612323008309
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2023.104458?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Yi & Hu, Ailing & Wang, Jiahua & Zhang, Yaojie, 2022. "Detection of fraud statement based on word vector: Evidence from financial companies in China," Finance Research Letters, Elsevier, vol. 46(PB).
    2. Li, Xiaochong & Li, Yanxi, 2020. "Female independent directors and financial irregularities in chinese listed firms: From the perspective of audit committee chairpersons," Finance Research Letters, Elsevier, vol. 32(C).
    3. Ouyang, Zi-sheng & Yang, Xi-te & Lai, Yongzeng, 2021. "Systemic financial risk early warning of financial market in China using Attention-LSTM model," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    4. Achakzai, Muhammad Atif Khan & Juan, Peng, 2022. "Using machine learning Meta-Classifiers to detect financial frauds," Finance Research Letters, Elsevier, vol. 48(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yuanying Chi & Mingjian Yan & Yuexia Pang & Hongbo Lei, 2022. "Financial Risk Assessment of Photovoltaic Industry Listed Companies Based on Text Mining," Sustainability, MDPI, vol. 14(19), pages 1-17, September.
    2. Guansan Du & Frank Elston, 2022. "RETRACTED ARTICLE: Financial risk assessment to improve the accuracy of financial prediction in the internet financial industry using data analytics models," Operations Management Research, Springer, vol. 15(3), pages 925-940, December.
    3. An, Hui & Wang, Hao & Delpachitra, Sarath & Cottrell, Simon & Yu, Xiao, 2022. "Early warning system for risk of external liquidity shock in BRICS countries," Emerging Markets Review, Elsevier, vol. 51(PA).
    4. Ouyang, Zisheng & Lu, Min & Lai, Yongzeng, 2023. "Forecasting stock index return and volatility based on GAVMD- Carbon-BiLSTM: How important is carbon emission trading?," Energy Economics, Elsevier, vol. 128(C).
    5. Lei, Yu-Tian & Ma, Chao-Qun & Ren, Yi-Shuai & Chen, Xun-Qi & Narayan, Seema & Huynh, Anh Ngoc Quang, 2023. "A distributed deep neural network model for credit card fraud detection," Finance Research Letters, Elsevier, vol. 58(PC).
    6. Chen, Dangxing & Ye, Jiahui & Ye, Weicheng, 2023. "Interpretable selective learning in credit risk," Research in International Business and Finance, Elsevier, vol. 65(C).
    7. Hongyan Geng & Maoyong Cheng & Junrui Zhang, 2021. "Effects of wealth management products on bank risk in China: The role of audit committee effectiveness," Pacific Economic Review, Wiley Blackwell, vol. 26(5), pages 575-616, December.
    8. Li, Jing & Li, Nan & Xia, Tongshui & Guo, Jinjin, 2023. "Textual analysis and detection of financial fraud: Evidence from Chinese manufacturing firms," Economic Modelling, Elsevier, vol. 126(C).
    9. Khan, Mushtaq Hussain & Fraz, Ahmad & Hassan, Arshad & Abedifar, Pejman, 2020. "Female board representation, risk-taking and performance: Evidence from dual banking systems," Finance Research Letters, Elsevier, vol. 37(C).
    10. Esra Atabay & Engin Boztepe, 2020. "The Effect of Gender Mainstreaming on Income of Turkish Independent Audit Organizations within the Scope of Sustainable Development," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(3), pages 111-127.
    11. Chen, Shihua & Chen, Yulin & Jebran, Khalil, 2023. "Beyond the glass ceiling: Informal gender-based status hierarchy and corporate misconduct," Journal of Business Research, Elsevier, vol. 169(C).
    12. Yang, Xite & Zhang, Qin & Liu, Haiyue & Liu, Zihan & Tao, Qiufan & Lai, Yongzeng & Huang, Linya, 2024. "Economic policy uncertainty, macroeconomic shocks, and systemic risk: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 69(PA).
    13. Zhou, Ying & Li, Haoran & Xiao, Zhi & Qiu, Jing, 2023. "A user-centered explainable artificial intelligence approach for financial fraud detection," Finance Research Letters, Elsevier, vol. 58(PA).
    14. Pallab Kumar Biswas & Larelle Chapple & Helen Roberts & Kevin Stainback, 2023. "Board Gender Diversity and Women in Senior Management," Journal of Business Ethics, Springer, vol. 182(1), pages 177-198, January.
    15. Kim Long Tran & Hoang Anh Le & Cap Phu Lieu & Duc Trung Nguyen, 2023. "Machine Learning to Forecast Financial Bubbles in Stock Markets: Evidence from Vietnam," IJFS, MDPI, vol. 11(4), pages 1-18, November.
    16. Sun, Xiaojun & Lei, Yalin, 2021. "Research on financial early warning of mining listed companies based on BP neural network model," Resources Policy, Elsevier, vol. 73(C).
    17. Han, Chen & Wu, Chengliang & Wei, Lu, 2023. "The impact of the disclosure characteristics of the application material on the successful listing of companies on China’s Science and Technology Innovation Board," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    18. Zhao, Zichao & Li, Dexuan & Dai, Wensheng, 2023. "Machine-learning-enabled intelligence computing for crisis management in small and medium-sized enterprises (SMEs)," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    19. Ali Amin & Ramiz ur Rehman & Rizwan Ali & Ridzwana Mohd Said, 2022. "Corporate Governance and Capital Structure: Moderating Effect of Gender Diversity," SAGE Open, , vol. 12(1), pages 21582440221, March.
    20. Zhang, Wenyu, 2024. "Dynamic monitoring of financial security risks: A novel China financial risk index and an early warning system," Economics Letters, Elsevier, vol. 234(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:finlet:v:58:y:2023:i:pb:s1544612323008309. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.