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http://dx.doi.org/10.3837/tiis.2022.03.014

Hybrid Fraud Detection Model: Detecting Fraudulent Information in the Healthcare Crowdfunding  

Choi, Jaewon (Department of Business Administration, Soonshunhyang University)
Kim, Jaehyoun (Department of Computer Education, Sungkyunkwan University)
Lee, Ho (Department of Future Technology, Korea University of Technology and Education)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.16, no.3, 2022 , pp. 1006-1027 More about this Journal
Abstract
In the crowdfunding market, various crowdfunding platforms can offer founders the possibilities to collect funding and launch someone's next campaign, project or events. Especially, healthcare crowdfunding is a field that is growing rapidly on health-related problems based on online platforms. One of the largest platforms, GoFundMe, has raised US$ 5 billion since 2010. Unfortunately, while providing crucial help to care for many people, it is also increasing risk of fraud. Using the largest platform of crowdfunding market, GoFundMe, we conduct an exhaustive search of detection on fraud from October 2016 to September 2019. Data sets are based on 6 main types of medical focused crowdfunding campaigns or events, such as cancer, in vitro fertilization (IVF), leukemia, health insurance, lymphoma and, surgery type. This study evaluated a detect of fraud process to identify fraud from non-fraud healthcare crowdfunding campaigns using various machine learning technics.
Keywords
Crowdfunding; Fraud Detection; Collaborative Filtering; Social SVD; LDA;
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