資本市場における不審な取引パターンを特定するクラスタリングベースのフレームワーク
A Clustering-Based Framework for Identifying Suspicious Trading Patterns in Capital Market
約100万件の金融取引データにK-Means++クラスタリングを適用し、不正取引の検出と分類を行う教師なしフレームワークを提案した。
著者: Asif Zaman, Romona Magdalene Sarkar, Sabiha Khair Ohi, Iftekharul Mobin
分類: cs.AI, cs.LG
原文アブストラクト
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.