Bibliometric Analysis of the Machine Learning Applications in Fraud Detection on Crowdfunding Platforms

Luis F. Cardona, Jaime A. Guzmán-Luna () and Jaime A. Restrepo-Carmona
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Luis F. Cardona: Facultad de Minas, Universidad Nacional de Colombia Sede Medellín, Medellín 050001, Colombia
Jaime A. Guzmán-Luna: Facultad de Minas, Universidad Nacional de Colombia Sede Medellín, Medellín 050001, Colombia
Jaime A. Restrepo-Carmona: Facultad de Minas, Universidad Nacional de Colombia Sede Medellín, Medellín 050001, Colombia

JRFM, 2024, vol. 17, issue 8, 1-23

Abstract: Crowdfunding platforms are important for startups, since they offer diverse financing options, market validation, and promotional opportunities through an investor community. These platforms provide detailed company information, aiding informed investment decisions within a regulated and secure environment. Machine learning (ML) techniques are important in analyzing large data sets, detecting anomalies and fraud, and enhancing decision-making and business strategies. A systematic review employed PRISMA guidelines, which studied how ML improves fraud detection on digital crowdfunding platforms. The analysis includes English-language studies from peer-reviewed journals published between 2018 and 2023 to analyze the pre- and post-COVID-19 pandemic. The findings indicate that ML techniques such as Random Forest, Support Vector Machine, and Artificial Neural Networks significantly enhance the predictive accuracy and utility of tax planning for startups considering equity crowdfunding. The United States, Germany, Canada, Italy, and Turkey do not present statistically significant differences at the 95% confidence level, standing out for their notable academic visibility. Florida Atlantic and Cornell Universities, Springer and John Wiley & Sons Ltd. publishing houses, and the Journal of Business Ethics and Management Science magazines present the highest citations without statistical differences at the 95% confidence level.

Keywords: crowdfunding; fraud; machine learning; PRISMA; statistical analysis (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
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