  Applications of blockchain technologies got a lot of attention in recent
years. They exceed beyond exchanging value and being a substitute for fiat
money and traditional banking system. Nevertheless, being able to exchange
value on a blockchain is at the core of the entire system and has to be
reliable. Blockchains have built-in mechanisms that guarantee whole system's
consistency and reliability. However, malicious actors can still try to steal
money by applying well known techniques like malware software or fake emails.
In this paper we apply supervised learning techniques to detect fraudulent
accounts on Ethereum blockchain. We compare capabilities of Random Forests,
Support Vector Machines and XGBoost classifiers to identify such accounts
basing on a dataset of more than 300 thousands accounts. Results show that we
are able to achieve recall and precision values allowing for the designed
system to be applicable as an anti-fraud rule for digital wallets or currency
exchanges. We also present sensitivity analysis to show how presented models
depend on particular feature and how lack of some of them will affect the
overall system performance.
