{"ID":5937640,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-08T08:55:49.227506586Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.04184","arxiv_id":"2607.04184","title":"A Clustering-Based Framework for Identifying Suspicious Trading Patterns in Capital Market","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. We implemented an unsupervised fraud-detection toolkit that begins with K-Means++ clustering to address this issue. A dataset of roughly one million financial transactions from 2012 to 2024 is used. In order to identify fraudulent trades and categorize them using market practice heuristic thresholds, the study suggests a clustering-based pipeline. The method highlights 2.02% of trades as suspicious where 51.10% clearly indicate spoofing, 0.10% indicate pump and dump, 0.55% indicate insider trading, 1.43% indicate a fake breakout, and 46.83% are unclassified. Despite the lack of ground truth, the model's performance is confirmed by a Silhouette Score of 0.561.","short_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. We implemented an unsupervised fraud-detection toolkit that begins with K-Means++ clustering to address this issue. A dataset of roughly one million financial t...","url_abs":"https://arxiv.org/abs/2607.04184","url_pdf":"https://arxiv.org/pdf/2607.04184v1","authors":"[\"Asif Zaman\",\"Romona Magdalene Sarkar\",\"Sabiha Khair Ohi\",\"Iftekharul Mobin\"]","published":"2026-07-05T09:02:34Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false}