A combined deep learning approach for fraudulent detection in the financial sector

Nripendra Narayan Das, C. Sivashanmugam and Sameer Shekhar

International Journal of Electronic Finance, 2025, vol. 14, issue 4, 502-523

Abstract: One of the crucial topics in the banking sector is fraud detection (FD). The involvement of fraud has significantly increased as new technologies are introduced daily. The main reason why current algorithms cannot accurately identify fraud is because of ineffective feature learning and prediction methods. Most work only concentrated on a few parameters to identify fraud. In reality though, fraudsters quickly change their identities and other traits. By integrating the two data mining techniques of optimum feature learning (OFL) and precise classification methodologies, this research finds a solution to the issue. The tri-teaching learning (TTL) optimisation approach is used after the system initially gathers the financial data. Then, the fully recurrent neural network (FRNN) algorithm divides the data into legitimate and fraudulent categories. In trials, the proposed feature learning and deep detection methodology improves accuracy (around 98%), precision (93%), and recall (92%).

Keywords: fraud detection; data mining; deep learning; financial sector; feature extraction; fully recurrent neural network; FRNN; optimum feature learning; OFL; tri-teaching learning; TTL; quality education. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=149159 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijelfi:v:14:y:2025:i:4:p:502-523

Access Statistics for this article

More articles in International Journal of Electronic Finance from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().