Publicação
Segmenting economic agents on the XRP Ledger: an unsupervised learning approach
| datacite.subject.fos | Ciências Sociais::Economia e Gestão | pt_PT |
| dc.contributor.advisor | Zejnilovic, Leid | |
| dc.contributor.author | Giesbrecht, Markus | |
| dc.date.accessioned | 2025-08-04T11:24:01Z | |
| dc.date.available | 2025-08-04T11:24:01Z | |
| dc.date.issued | 2025-01-24 | |
| dc.date.submitted | 2024-12-17 | |
| dc.description.abstract | XRP is among the top 5 most prevalent cryptocurrencies as of 2024. However, the pseudonymous nature of XRP Ledger transactions complicates the understanding of economic activities and regulatory oversight of fraud within the network. This study uses descriptive statistics to develop heuristics for categorizing distinct types of economic agent activities and leverages unsupervised machine learning to segment agents into clusters; successfully distinguishing accounts such as decentralized exchanges, gambling sites, and NFT-related entities. Additionally, supervised fraud detection models are trained with an off-chain dataset of accounts involved in spam, token theft, and Ponzi schemes, achieving fraud detection accuracies of over 90%. | pt_PT |
| dc.identifier.tid | 203962877 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/185999 | |
| dc.language.iso | eng | pt_PT |
| dc.relation | UID/ECO/00124/2013 | pt_PT |
| dc.subject | Behavioral analysis | pt_PT |
| dc.subject | Blockchain | pt_PT |
| dc.subject | Fraud detection | pt_PT |
| dc.subject | Heuristics | pt_PT |
| dc.subject | Ledgerlytics | pt_PT |
| dc.subject | Machine learning | pt_PT |
| dc.subject | Ripple | pt_PT |
| dc.subject | Segmentation | pt_PT |
| dc.subject | Supervised | pt_PT |
| dc.subject | Unsupervised | pt_PT |
| dc.subject | XRP Ledger | pt_PT |
| dc.title | Segmenting economic agents on the XRP Ledger: an unsupervised learning approach | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | A work project, presented as part of the requirements for the Award of a Master’s degree in Business Analytics from the Nova School of Business and Economics | pt_PT |