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Link to original content: https://doi.org/10.1007/s13198-023-02043-7
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Review the role of artificial intelligence in detecting and preventing financial fraud using natural language processing

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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.

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  1. Chitkara Business School, Chitkara University, Rajpura, India

    Pallavi Sood, Shivinder Nijjer & Sumit Sakhuja

  2. upGrad Campus, upGrad Education Private Limited, Bangalore, India

    Chetan Sharma

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Sood, P., Sharma, C., Nijjer, S. et al. Review the role of artificial intelligence in detecting and preventing financial fraud using natural language processing. Int J Syst Assur Eng Manag 14, 2120–2135 (2023). https://doi.org/10.1007/s13198-023-02043-7

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