Integration of NLP, AI-driven data analysis, risk assessment, and electronic whistle-blowing systems in fraud detection

Chelsea Tan (), Calrsen Cyntia () and Bambang Leo Handoko ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 6, 835-845

Abstract: The rapid development of technology in Industry 4.0 today has encouraged the integration of Artificial Intelligence (AI), the Internet of Things (IoT), and big data in helping the operations of various industrial sectors, especially in the start-up sector. This study aims to determine whether there are factors such as Natural Language Processing, AI-driven data analysis, risk assessment, and electronic whistleblowing systems that will affect the way the system detects fraud, and to determine whether these factors cause several start-up companies to use the integration of AI, NLP, and E-WBS to accelerate the fraud disclosure process. This study involved 113 employee respondents who worked in start-up companies. The results of the respondent data were processed using SMART-PLS 4.0, which involved the reliability and validity methods, discriminant analysis, r-squared adjusted, and outer loading. The results of the study showed that Natural Language Processing, AI-driven data analysis, risk assessment, and electronic whistleblowing systems did have a positive impact or increase the accuracy of fraud disclosure in real-time, effectively, and efficiently. Early identification of fraud patterns can prevent greater losses, and parties who are aware of fraudulent actions will report them and have reporting channels that create a sense of security for the reporter.

Keywords: AI-driven data analysis; Natural language processing; Whistle blowing systems. (search for similar items in EconPapers)
Date: 2025
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