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
Download full text from publisher
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.
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:gam:jmathe:v:12:y:2024:i:14:p:2195-:d:1434237. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.