# Dissecting Ponzi schemes on Ethereum: identification, analysis, and impact

arXiv: https://arxiv.org/abs/1703.03779 · HTML: https://mlsanity.com/paper/1703.03779/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2017.

## Built on by (most cited first, 19 of 19)

- [Self-supervised Incremental Deep Graph Learning for Ethereum Phishing Scam Detection](https://mlsanity.com/paper/2106.10176/) (2021)
- [Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application of Machine Learning-based Forensics](https://mlsanity.com/paper/2206.04803/) (2022)
- [Blockchain Phishing Scam Detection via Multi-channel Graph Classification](https://mlsanity.com/paper/2108.08456/) (2021)
- [Time-aware Metapath Feature Augmentation for Ponzi Detection in Ethereum](https://mlsanity.com/paper/2210.16863/) (2022)
- [Do not rug on me: Zero-dimensional Scam Detection](https://mlsanity.com/paper/2201.07220/) (2022)
- [Giving AI Agents Access to Cryptocurrency and Smart Contracts Creates New Vectors of AI Harm](https://mlsanity.com/paper/2507.08249/) (2025)
- [Explainable Ponzi Schemes Detection on Ethereum](https://mlsanity.com/paper/2301.04872/) (2023)
- [Machine Learning for Blockchain Data Analysis: Progress and Opportunities](https://mlsanity.com/paper/2404.18251/) (2024)
- [SolRPDS: A Dataset for Analyzing Rug Pulls in Solana Decentralized Finance](https://mlsanity.com/paper/2504.07132/) (2025)
- [Data-driven Smart Ponzi Scheme Detection](https://mlsanity.com/paper/2108.09305/) (2021)
- [SourceP: Detecting Ponzi Schemes on Ethereum with Source Code](https://mlsanity.com/paper/2306.01665/) (2023)
- [Enhancing Ethereum Fraud Detection via Generative and Contrastive Self-supervision](https://mlsanity.com/paper/2408.00641/) (2024)
- [Early Rug Pull Warning for BSC Meme Tokens via Multi-Granularity Wash-Trading Pattern Profiling](https://mlsanity.com/paper/2603.13830/) (2026)
- [Paper Agents, Paper Gains: An Empirical Analysis of DeFi Investment Agents](https://mlsanity.com/paper/2605.29174/) (2026)
- [Detecting DeFi Securities Violations from Token Smart Contract Code](https://mlsanity.com/paper/2112.02731/) (2021)
- [Anomaly Detection for Fraud in Cryptocurrency Time Series](https://mlsanity.com/paper/2207.11466/) (2022)
- [Improving the Accuracy of Transaction-Based Ponzi Detection on Ethereum](https://mlsanity.com/paper/2308.16391/) (2023)
- [Linking Cryptoasset Attribution Tags to Knowledge Graph Entities: An LLM-based Approach](https://mlsanity.com/paper/2502.10453/) (2025)
- [CASPER: Contrastive Approach for Smart Ponzi Scheme Detecter with More Negative Samples](https://mlsanity.com/paper/2507.16840/) (2025)

## Abstract (arXiv)

Ponzi schemes are financial frauds which lure users under the promise of high profits. Actually, users are repaid only with the investments of new users joining the scheme: consequently, a Ponzi scheme implodes soon after users stop joining it. Originated in the offline world 150 years ago, Ponzi schemes have since then migrated to the digital world, approaching first the Web, and more recently hanging over cryptocurrencies like Bitcoin. Smart contract platforms like Ethereum have provided a new opportunity for scammers, who have now the possibility of creating "trustworthy" frauds that still make users lose money, but at least are guaranteed to execute "correctly". We present a comprehensive survey of Ponzi schemes on Ethereum, analysing their behaviour and their impact from various viewpoints.