Detecting Fraudulent Accounts on Blockchain: A Supervised Approach

Ostapowicz, Michal; Żbikowski, Kamil

doi:10.48550/arxiv.1908.07886

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Machine Learning For Anti-money Laundering1

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2023

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“…Alarab et al (2020a) used ensemble learning and showed better results than Weber et al's method. Ostapowicz andŻbikowski (2019) andFawaz Ibrahim et al (2021) proposed ML models to identify fraudulent accounts on Ethereum, with RF having the best performance in recall and false-positive rate. However, Ostapowicz and Żbikowski (2019) cautioned that the recall of 85% obtained may not be adequate for a real-world anti-fraud system.…”

Section: Machine Learning For Anti-money Launderingmentioning

confidence: 99%

Fraud Detection and Anti-Money Laundering Applying Machine Learning Techniques in Cryptocurrency Transactional Graphs

2023

Proceedings of the 16 Th International Conference on ICT, Society and Human Beings (ICT 2023), the 15 Th International Conferen

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Cryptocurrencies have advantages such as lower costs, efficiency, and security, but are vulnerable to fraud due to a lack of controls and anonymity. Criminals use virtual currencies for quick, anonymous transactions. Robust measures are needed to prevent illegal activities like money laundering. Machine learning (ML) and graph analysis can help detect fraud in the cryptocurrency market, despite criminals mimicking normal transactions. This study aims to use cutting-edge technologies like ML and graph learning to find fraudulent patterns in cryptocurrency transactions.

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Section: Machine Learning For Anti-money Launderingmentioning

confidence: 99%

Fraud Detection and Anti-Money Laundering Applying Machine Learning Techniques in Cryptocurrency Transactional Graphs

2023

Proceedings of the 16 Th International Conference on ICT, Society and Human Beings (ICT 2023), the 15 Th International Conferen

View full text Add to dashboard Cite

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Detecting Fraudulent Accounts on Blockchain: A Supervised Approach

Cited by 1 publication

References 0 publications

Fraud Detection and Anti-Money Laundering Applying Machine Learning Techniques in Cryptocurrency Transactional Graphs

Fraud Detection and Anti-Money Laundering Applying Machine Learning Techniques in Cryptocurrency Transactional Graphs

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