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 ΜέγεθοςΜορφότυπος
SSRN Crowdfunding.pdf1.16 MBAdobe PDFΔείτε/ Ανοίξτε
CORE Recommender
Δείξε την πλήρη περιγραφή του τεκμηρίου

Page view(s)

13
checked on 13 Νοε 2024

Download(s)

10
checked on 13 Νοε 2024

Google ScholarTM

Check

Altmetric


Αυτό το τεκμήριο προστατεύεται από άδεια Άδεια Creative Commons Creative Commons