arXiv Machine Learning
Topological Fraud Detection in Latent Transaction Spaces
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Hybrid LSTM-Graph Neural Framework for Robust Financial Fraud Detection and Adversarial Resilience
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.
arXiv AI
Jul 7
A Clustering-Based Framework for Identifying Suspicious Trading Patterns in Capital Market
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.
arXiv Machine Learning
Jul 14
Quantum-Inspired Contextual Learning for Sparse-Ring Fraud Detection in Dynamic Transaction Graphs
arXiv:2607. 09704v1 Announce Type: new Abstract: We present an exploratory benchmark and quantum-inspired modeling prototype for fraud screening in dynamic financial transaction graphs.
arXiv Machine Learning
Sep 24
Quality over Quantity: Semi-Supervised Detection of Illicit Bitcoin Flows via Feature Engineering
The paper presents a semi‑supervised learning framework for detecting illicit Bitcoin flows in Shared Send Mixers, using a large historical dataset of 163 million transactions. It demonstrates that the success of SSL depends on data quality rather than sheer volume, with high‑fidelity features such as KeyLinker address clustering and Shared Send Untangling complexity metrics achieving an F1 score of 0.84 on unlabeled data. The study also shows that common heuristics like One‑Time Change introduce noise, underscoring the importance of smarter feature engineering in blockchain forensics.
Hugging Face Trending Papers
Sep 1
Refining Heuristic-Based Bitcoin Address Clustering with Graph Neural Networks
Bitcoin's pseudonymous nature makes it challenging to analyze user-level activity, since a single user may control multiple identifiers (addresses). Existing heuristic-based methods attempt to identif...
arXiv Machine Learning
Sep 3
Refining Heuristic-Based Bitcoin Address Clustering with Graph Neural Networks
The paper introduces a method to improve heuristic-based Bitcoin address clustering by using graph neural networks to generate contrastive embeddings. It releases a large Bitcoin transaction graph dataset, presents a learning framework that aligns embeddings with existing heuristics, and applies hierarchical clustering to refine clusters and detect suspicious merges. The approach offers a more modular and theoretically grounded way to analyze user-level activity on the blockchain.