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:2608. 20271v1 Announce Type: new Abstract: The rapid proliferation of memecoins on blockchain platforms has increased the risk of fraudulent activities, particularly rug pulls.
By Jianghai Li, Pavel Kuznetsov, Yury Yanovich, Konstantin Nott-Whaley, Igor Vodolazov
arXiv:2607. 13469v1 Announce Type: cross Abstract: The banking sector increasingly relies on automated systems to monitor electronic transactions for signs of fraud, yet conventional rule-based approaches struggle with high false-positive rates and offer no justification for their outputs, limiting their utility for compliance teams.
By Anupa Lodhi
The paper explores a hybrid approach that combines traditional Journal Entry Tests (JETs) with machine learning techniques to enhance anomaly detection in general ledger data. It presents specialized models designed to improve the accuracy and validity of detected anomalies, thereby aiming to reduce false positives and increase audit efficiency. Experiments are conducted using synthetic data that includes both normal and anomalous journal entries.
By Jan Gronewald, Alexander Michael Rombach, Sebastian Stephan, Peter Fettke
arXiv:2607. 19266v1 Announce Type: cross Abstract: Fraud detection systems must scale with rising transaction volume while remaining explainable and reviewable.
By Rahil Sharma
arXiv:2606. 16663v1 Announce Type: new Abstract: Money laundering through insurance claims poses a threat to insurers both through fraudulent payouts and reputational and regulatory risk.
By Dara Goldar, Geir Kjetil Ferkingstad Sandve, Martin Jullum
The paper introduces Centroid‑Guided Contrastive Loss (CGCL), a novel loss function that combines classification with dense clustering to improve fraudulent job posting detection. CGCL reshapes the latent space using a centroid‑driven top‑k push‑and‑pull mechanism, ensuring accurate decision boundaries while preserving compact clustering of similar fake posts. Experiments on the EMSCAD benchmark show that CGCL achieves state‑of‑the‑art performance.
By Syed Ali Ahmed (National University of Computer and Emerging Sciences, Karachi, Pakistan), Malaika Raza (National University of Computer and Emerging Sciences, Karachi, Pakistan), Muhammad Shoaib Siddiqui (Islamic University of Madinah, Madinah, Saudi Arabia), Muhammad Rafi (National University of Computer and Emerging Sciences, Karachi, Pakistan)
arXiv:2606. 17555v1 Announce Type: cross Abstract: Banks simultaneously face signature-based fraud (card-not-present attacks, account takeover, ATM cloning) and behavioural financial crime (structuring, layering, mule networks, business email compromise) -- two threat families with fundamentally different detection requirements.
By Joseph Walusimbi, Joshua Benjamin Ssentongo
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:2601. 11073v3 Announce Type: replace-cross Abstract: Online financial services constitute an essential component of contemporary web ecosystems, yet their openness introduces substantial exposure to fraud that harms vulnerable users and weakens trust in digital finance.
By Rongkun Cui, Nana Zhang, Kun Zhu, Qi Zhang
arXiv:2609.08445v1 Announce Type: new
Abstract: Working entirely on topologically anonymized embeddings, we perform fraud detection using iterative rounds of unsupervised filtering followed by superv...
By Avraham Bourla
The paper introduces an autonomous AI security agent designed to detect multi‑vector fraud and anti‑money‑laundering (AML) threats in both retail and corporate banking. It operates across two parallel event streams—transactions and sessions—using a fusion of LSTM behaviour models, statistical velocity monitors, and graph‑based account‑counterparty analysis. Experiments on a synthetic dataset show the agent outperforms rule‑based and LSTM‑only baselines, achieving F1 scores of 0.787 for transactions and 0.867 for sessions, while also providing rapid, low‑latency responses and supporting customer verification and analyst assistance.
By Joseph Walusimbi, Joshua Benjamin Ssentongo