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An Intelligent Financial Fraud Detection Support System Based on Three-Level Relationship Penetration

Author

Listed:
  • Xiang Li

    (Smart State Governance Laboratory, Shandong University, Qingdao 266237, China
    School of Information Science and Engineering, Shandong University, Qingdao 266237, China)

  • Lei Chu

    (Smart State Governance Laboratory, Shandong University, Qingdao 266237, China
    School of Information Science and Engineering, Shandong University, Qingdao 266237, China
    School of Political Science and Public Administration, Shandong University, Qingdao 266237, China)

  • Yujun Li

    (Smart State Governance Laboratory, Shandong University, Qingdao 266237, China
    School of Information Science and Engineering, Shandong University, Qingdao 266237, China)

  • Zhanjun Xing

    (Smart State Governance Laboratory, Shandong University, Qingdao 266237, China
    School of Political Science and Public Administration, Shandong University, Qingdao 266237, China)

  • Fengqian Ding

    (School of Information Science and Engineering, Shandong University, Qingdao 266237, China)

  • Jintao Li

    (Smart State Governance Laboratory, Shandong University, Qingdao 266237, China
    School of Political Science and Public Administration, Shandong University, Qingdao 266237, China)

  • Ben Ma

    (Smart State Governance Laboratory, Shandong University, Qingdao 266237, China
    School of Political Science and Public Administration, Shandong University, Qingdao 266237, China)

Abstract

Financial fraud is a serious challenge in a rapidly evolving digital economy that places increasing demands on detection systems. However, traditional methods are often limited by the dimensional information of the corporations themselves and are insufficient to deal with the complexity and dynamics of modern financial fraud. This study introduces a novel intelligent financial fraud detection support system, leveraging a three-level relationship penetration (3-LRP) method to decode complex fraudulent networks and enhance prediction accuracy, by integrating the fuzzy rough density-based feature selection (FRDFS) methodology, which optimizes feature screening in noisy financial environments, together with the fuzzy deterministic soft voting (FDSV) method that combines transformer-based deep tabular networks with conventional machine learning classifiers. The integration of FRDFS optimizes feature selection, significantly improving the system’s reliability and performance. An empirical analysis, using a real financial dataset from Chinese small and medium-sized enterprises (SMEs), demonstrates the effectiveness of our proposed method. This research enriches the financial fraud detection literature and provides practical insights for risk management professionals, introducing a comprehensive framework for early warning and proactive risk management in digital finance.

Suggested Citation

  • Xiang Li & Lei Chu & Yujun Li & Zhanjun Xing & Fengqian Ding & Jintao Li & Ben Ma, 2024. "An Intelligent Financial Fraud Detection Support System Based on Three-Level Relationship Penetration," Mathematics, MDPI, vol. 12(14), pages 1-23, July.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:14:p:2195-:d:1434237
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    References listed on IDEAS

    as
    1. Milad Soltani & Alexios Kythreotis & Arash Roshanpoor, 2023. "Two decades of financial statement fraud detection literature review; combination of bibliometric analysis and topic modeling approach," Journal of Financial Crime, Emerald Group Publishing Limited, vol. 30(5), pages 1367-1388, April.
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