• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

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

Vol. 16, No. 3, March 31, 2022
10.3837/tiis.2022.03.014, Download Paper (Free):

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.


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Cite this article

[IEEE Style]
J. Choi, J. Kim, H. Lee, "Hybrid Fraud Detection Model: Detecting Fraudulent Information in the Healthcare Crowdfunding," KSII Transactions on Internet and Information Systems, vol. 16, no. 3, pp. 1006-1027, 2022. DOI: 10.3837/tiis.2022.03.014.

[ACM Style]
Jaewon Choi, Jaehyoun Kim, and Ho Lee. 2022. Hybrid Fraud Detection Model: Detecting Fraudulent Information in the Healthcare Crowdfunding. KSII Transactions on Internet and Information Systems, 16, 3, (2022), 1006-1027. DOI: 10.3837/tiis.2022.03.014.

[BibTeX Style]
@article{tiis:25528, title="Hybrid Fraud Detection Model: Detecting Fraudulent Information in the Healthcare Crowdfunding", author="Jaewon Choi and Jaehyoun Kim and Ho Lee and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2022.03.014}, volume={16}, number={3}, year="2022", month={March}, pages={1006-1027}}