Παρακαλώ χρησιμοποιήστε αυτό το αναγνωριστικό για να παραπέμψετε ή να δημιουργήσετε σύνδεσμο προς αυτό το τεκμήριο:
https://hdl.handle.net/20.500.14279/33164
Τίτλος: | Crowdfunding Fraud Detection: A Systematic Review Highlights AI and Blockchain using Topic Modeling | Συγγραφείς: | Machado, Marcos Coita, Ioana Coita Teijeiro, Lucia Gomez Gregoriades, Andreas Themistocleous, Christos Heeswijk, Wouter Bernard, Frederik Sinan Muñiz, José Antonio Bolesta, Karolina Osterrieder, Joerg Liu, Yiting Dubrovska, Anastasija Stanca, Liana Stanca, Liana Aydin, Nadi Serhan Rupeika-Apoga, Ramona Teng, Huei-Wen Yilmaz, Gokce Nur Péliová, Jana Alexy, Martin Tidjani, Chemseddine Mare, Codruta Filipovska, Olivija |
Major Field of Science: | Social Sciences | Field Category: | Economics and Business | Λέξεις-κλειδιά: | Fraud Detection;Crowdfunding;Lending Settings;Finance Industry;Alternative Finance Methods | Ημερομηνία Έκδοσης: | 10-Οκτ-2024 | Πηγή: | SSRN | Περίληψη: | Crowdfunding platforms have gained popularity as a means of financing entrepreneurial initiatives but face a high risk of fraud. Fraud is a significant problem due to its impact on trust, ultimately leading to financial instability. Detecting and preventing fraud is therefore paramount for the sustainability of crowdfunding platforms. This study provides a systematic review of the literature and state-of-the-art discussions about crowdfunding fraud. Unsupervised topic modeling highlights that both AI and blockchain are recurrently presented in the literature as effective methodologies for identifying and preventing fraudulent practices. Furthermore, this work describes current market practices of crowdfunding platforms in preventing fraudulent behavior and argues that, while fraud is rare, its high impact necessitates new and innovative forms of fraud detection. A key limiting factor for the application of AI solutions is the lack of available labeled crowdfunding data for training efficient algorithms for fraud detection, which is crucial as it constitutes an anomaly detection machine learning task. In this context, unsupervised machine learning methods are discussed as valuable techniques for detecting anomalies in the absence of labeled fraud cases due to their ability to adapt to evolving fraud patterns. Altogether, this research provides valuable insights into the complexity of detecting and preventing fraudulent activities in crowdfunding and highlights effective detection techniques that, if implemented, offer promising solutions to enhance platform reputation and ensure regulatory compliance. | URI: | https://hdl.handle.net/20.500.14279/33164 | DOI: | 10.2139/ssrn.4948895 | Rights: | CC0 1.0 Universal | Type: | Article | Affiliation: | University of Twente University of Economics in Bratislava University of Geneva University of Hamburg Cyprus University of Technology University of Bath Warsaw School of Economics (SGH) Sofia University "St. Kliment Ohridski ” Babeş-Bolyai University Istinye University University of Latvia National Yang Ming Chiao Tung University TED University University of Economics in Bratislava Centre de Recherche en Économie Appliquée pour le Développement Geostrategic Institute Global |
Publication Type: | Peer Reviewed |
Εμφανίζεται στις συλλογές: | Άρθρα/Articles |
Αρχεία σε αυτό το τεκμήριο:
Αρχείο | Περιγραφή | Μέγεθος | Μορφότυπος | |
---|---|---|---|---|
SSRN Crowdfunding.pdf | 1.16 MB | Adobe PDF | Δείτε/ Ανοίξτε |
CORE Recommender
Page view(s)
20
Last Week
1
1
Last month
checked on 24 Νοε 2024
Download(s)
24
checked on 24 Νοε 2024
Google ScholarTM
Check
Altmetric
Αυτό το τεκμήριο προστατεύεται από άδεια Άδεια Creative Commons