A Clustering-Based Framework for Identifying Suspicious Trading Patterns in Capital Market
For financial regulators, this provides an unsupervised method to flag potential market manipulation, though it is incremental as it applies existing clustering techniques to a new dataset.
The paper proposes a clustering-based pipeline using K-Means++ to detect suspicious trading patterns in capital markets, identifying 2.02% of transactions as suspicious with a Silhouette Score of 0.561.
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.