The paper presents a method for early detection of fraudulent memecoins (rug pulls) on the Solana blockchain, using a dataset of 6.4 million tokens collected over seven months. It shows that most rug pulls occur within an hour of launch and that classic machine learning models, especially Gradient Boosting (XGBoost), can reliably predict them using only the first five minutes of trading data. Cross‑platform data fusion between PumpFun and Raydium further improves detection by reducing domain shift.
arXiv:2607. 27350v1 Announce Type: new Abstract: Sybil bots are Ethereum actors that imitate legitimate users to extract airdrop rewards or influence governance.
By Micha{\l} Bartnicki, Jaros{\l}aw A. Chudziak
arXiv:2606. 25007v1 Announce Type: new Abstract: Financial fraud detection in digital banking requires reasoning over multiple heterogeneous event streams -- transactions, login sessions, risk signals -- that individually appear benign but collectively reveal fraudulent patterns.
By Mohammadamin Dashti Moghaddam, Nick Sciarrilli
arXiv:2608.29973v1 Announce Type: new
Abstract: Cryptocurrency markets generate high-frequency, multi-source data that is expensive to work with unless a team already has commercial-grade streaming a...
By Basil Sajid Shaikh, Melrick Mascarenhas, Nuzhat Faiz Shaikh
arXiv:2607. 19350v1 Announce Type: new Abstract: Financial institutions face significant challenges in detecting sophisticated money laundering patterns, such as smurfing and layering, due to extreme data imbalance (0.
By Mariam Zakaria Moussa Ali
arXiv:2607. 17555v1 Announce Type: new Abstract: Predicting extreme price movements in high-frequency financial markets is a challenging task due to non-stationarity, heavy-tailed return distributions, and severe class imbalance.
By Maorufa Zaman, Haris Md Sahed
arXiv:2608. 12864v1 Announce Type: cross Abstract: Public blockchain data enables large-scale DeFi-related analysis, but many existing approaches are application-specific, difficult to scale, or hard to interpret.
By Dorottya Zelenyanszki, Zhe Hou, Kamanashis Biswas, Vallipuram Muthukkumarasamy
arXiv:2607. 04184v1 Announce Type: new Abstract: Market manipulation is the dubious practice of manipulating stock prices in order to make a quick profit, which truly degrades confidence on trading platforms.
By Asif Zaman, Romona Magdalene Sarkar, Sabiha Khair Ohi, Iftekharul Mobin
arXiv:2604. 17420v2 Announce Type: replace-cross Abstract: Money laundering poses severe risks to global financial systems, driving the widespread adoption of machine learning for transaction monitoring.
By Keyang Chen, Mingxuan Jiang, Yongsheng Zhao, Zeping Li, Zaiyuan Chen, Weiqi Luo, Zhixin Li, Sen Liu, Yinan Jing, Guangnan Ye, Xihong Wu, Hongfeng Chai
Predicting extreme price movements in high-frequency financial markets is a challenging task due to non-stationarity, heavy-tailed return distributions, and severe class imbalance. In particular, rare but impactful events are often difficult to detect using conventional modeling approaches, which typically treat extreme movements as isolated observations.
arXiv:2608. 15447v1 Announce Type: new Abstract: Mobile money has widened financial access across Sub-Saharan Africa and enlarged the surface for money-laundering and terrorism-financing (ML/TF) activity in ecosystems dominated by high-volume, low-value transactions.
By Emmanuel Nahimana, Ya\'e Ulrich Gaba
arXiv:2607. 27370v1 Announce Type: new Abstract: Sybil attackers are Blockchain actors that adopt the characteristics of regular users to exploit airdrops or influence governance.
By Micha{\l} Bartnicki, Jaros{\l}aw A. Chudziak