New AI framework detects blockchain-based smart Ponzi schemes with high accuracy.

Researchers from the University of Electronic Science and Technology of China, City University of Macau, and Swinburne University of Technology have developed a deep-learning-based framework called CASPER to detect smart Ponzi schemes on blockchain networks. CASPER uses a contrastive learning approach to compare similarities and differences between blockchain transactions without relying on labeled training data. This technique has the potential to improve the accuracy of Ponzi scheme detection in real-world settings.

Researchers from the University of Electronic Science and Technology of China, City University of Macau, and Swinburne University of Technology have developed a deep-learning-based framework called CASPER to detect smart Ponzi schemes on blockchain networks. CASPER uses a contrastive learning approach to compare similarities and differences between blockchain transactions without relying on labeled training data. This technique has the potential to improve the accuracy of Ponzi scheme detection in real-world settings [2].

The framework, detailed in a paper published on the arXiv preprint server, employs an approach known as contrastive learning. This machine learning technique allows a computational model to learn to tell different things apart by comparing them. By leveraging contrastive learning techniques, CASPER can learn more effective representations of smart contract source code using unlabeled datasets, significantly reducing both operational costs and system complexity [2].

The team assessed the new Ponzi scheme detection technique in a series of tests, in which it was fed blockchain transactions from a publicly available dataset. Their framework performed remarkably well, despite being trained on a limited amount of labeled data. "We evaluate CASPER on the XBlock dataset, where it outperforms the baseline by 2.3% in F1 score when trained with 100% labeled data," the team wrote. "More impressively, with only 25% labeled data, CASPER achieves an F1 score nearly 20% higher than the baseline under identical experimental conditions. These results highlight CASPER's potential for effective and cost-efficient detection of smart Ponzi schemes, paving the way for scalable fraud detection solutions in the future" [2].

In the future, the CASPER framework could be improved and tested on more real-world data, to further assess its potential for detecting and mitigating smart Ponzi schemes. Eventually, it could make its way into real-world settings, where it could help to protect digital currency investors against malicious activities.

References:

[1] https://en.coinotag.com/vaneck-jitosol-spot-etf-filing-could-boost-solana-staking-liquidity-jto-faces-2-resistance/
[2] https://techxplore.com/news/2025-08-contrastive-framework-blockchain-based-smart.html