# Significant Digits: Using Large-Scale Blockchain Data to Predict Fraudulent Addresses

arXiv: https://arxiv.org/abs/2301.01809 · HTML: https://mlsanity.com/paper/2301.01809/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2023.

## Built on by (most cited first, 1 of 1)

- [Benchmarking Temporal Web3 Intelligence: Lessons from the FinSurvival 2025 Challenge](https://mlsanity.com/paper/2602.23159/) (2026)

## Abstract (arXiv)

Blockchain systems and cryptocurrencies have exploded in popularity over the past decade, and with this growing user base, the number of cryptocurrency scams has also surged. Given the graphical structure of blockchain networks and the abundance of data generated on these networks, we use graph mining techniques to extract essential information on transactions and apply Benford's Law to extract distributional information on address transactions. We then apply a gradient-boosting tree model to predict fraudulent addresses. Our results show that our method can detect scams with reasonable accuracy and that the features generated based on Benford's Law are the most significant features.