  Blockchain technology has been successfully exploited for deploying new
economic applications. However, it has started arousing the interest of
malicious actors who deliver scams to deceive honest users and to gain economic
advantages. Ponzi schemes are one of the most common scams. Here, we present a
classifier for detecting smart Ponzi contracts on Ethereum, which can be used
as the backbone for developing detection tools. First, we release a labelled
data set with 4422 unique real-world smart contracts to address the problem of
the unavailability of labelled data. Then, we show that our classifier
outperforms the ones proposed in the literature when considering the AUC as a
metric. Finally, we identify a small and effective set of features that ensures
a good classification quality and investigate their impacts on the
classification using eXplainable AI techniques.
