# Explainable Ponzi Schemes Detection on Ethereum

arXiv: https://arxiv.org/abs/2301.04872 · HTML: https://mlsanity.com/paper/2301.04872/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 12 Jan 2023.

## Builds on (most cited first, 3 of 3)

- [Dissecting Ponzi schemes on Ethereum: identification, analysis, and impact](https://mlsanity.com/paper/1703.03779/) (2017)
- [Data mining for detecting Bitcoin Ponzi schemes](https://mlsanity.com/paper/1803.00646/) (2018)
- [Knowledge Discovery in Cryptocurrency Transactions: A Survey](https://mlsanity.com/paper/2010.01031/) (2020)

## Built on by (most cited first, 2 of 2)

- [Fishing for Phishers: Learning-Based Phishing Detection in Ethereum Transactions](https://mlsanity.com/paper/2504.17953/) (2025)
- [CASPER: Contrastive Approach for Smart Ponzi Scheme Detecter with More Negative Samples](https://mlsanity.com/paper/2507.16840/) (2025)

## Abstract (arXiv)

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.