Review the role of artificial intelligence in detecting and preventing financial fraud using natural language processing

Pallavi Sood (), Chetan Sharma (), Shivinder Nijjer () and Sumit Sakhuja ()
Additional contact information
Pallavi Sood: Chitkara University
Chetan Sharma: upGrad Education Private Limited
Shivinder Nijjer: Chitkara University
Sumit Sakhuja: Chitkara University

International Journal of System Assurance Engineering and Management, 2023, vol. 14, issue 6, No 8, 2120-2135

Abstract: Abstract Frauds accounted for significant losses in the financial sector and emerged as the industry’s biggest challenge. Companies invest significant amounts to prevent such fraud. It has been reported that 63.6% of the financial institutions that use Automated Fraud prevention methods successfully prevented frauds before their occurrence. Some estimations suggest that 80% of specialists are confident in cutting down fraud using Artificial Intelligence (AI)-based platforms. Several research studies have also administered AI-based techniques for fraud prevention. This study takes a systematic literature review approach to uncover the emerging areas of fraud detection using AI. The authors have analyzed 241 research articles published in the last 20 years. The Scopus database was the source of the articles in the literature review. The meta-analysis and network analysis were carried out, and the output shows the up trend of this research domain. Author-coauthor network collaboration is analyzed using the VOSviewer tool. K-means clustering was performed to identify the critical research domain, and future research areas were also identified. This research will act as a reference for future scholars who want to perform analysis on the application of AI techniques in financial fraud detection and prevention. We finally conclude the study by identifying the scope of future research and will be a value addition for financial fraud researchers.

Keywords: Automated techniques; Financial frauds; Topic modelling; KNIME; Artificial intelligence; Latent semantic analysis; Text mining; Vosviewer (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s13198-023-02043-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:ijsaem:v:14:y:2023:i:6:d:10.1007_s13198-023-02043-7

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-023-02043-7

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().