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Segmenting economic agents on the XRP Ledger: an unsupervised learning approach
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| 1.97 MB | Adobe PDF |
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Orientador(es)
Resumo(s)
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%.
Descrição
Palavras-chave
Behavioral analysis Blockchain Fraud detection Heuristics Ledgerlytics Machine learning Ripple Segmentation Supervised Unsupervised XRP Ledger