Integration of NLP, AI-Driven Data Analysis, Risk Assessment, and Electronic Whistle-Blowing Systems in Fraud Detection

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Abstract
The rapid development of technology in industry 4.0 today has encouraged the integration of Artificial Intelligence (AI), 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 whistle-blowing 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, r-square adjusted, and outer loading. The results of the study showed that Natural Language Processing, AI-Driven data analysis, risk assessment, and Electronic Whistle Blowing Systems did have a positive impact or increase the accuracy of fraud disclosure in real-time, effectively and efficiently. Keyword: AI-Driven Data Analysis, Natural Language Processing, Whistle-Blowing Systems

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