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AI framework identifies suspicious trading patterns using K-Means++ clustering

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new toolkit using K-Means++ clustering to detect suspicious trading patterns in capital markets. The framework analyzes a dataset of approximately one million financial transactions from 2012 to 2024, identifying 2.02% of trades as potentially fraudulent. The identified suspicious trades are categorized into types such as spoofing, pump and dump, insider trading, and fake breakouts, with a significant portion remaining unclassified due to the absence of ground truth data. The model's effectiveness is validated by a Silhouette Score of 0.561. AI

IMPACT Provides a novel AI-driven approach for detecting financial market manipulation, potentially improving market integrity.

RANK_REASON Academic paper detailing a new AI-driven framework for financial fraud detection. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI framework identifies suspicious trading patterns using K-Means++ clustering

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Academic paper detailing a new AI-driven framework for financial fraud detection. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Asif Zaman, Romona Magdalene Sarkar, Sabiha Khair Ohi, Iftekharul Mobin ·

    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. We implemented an unsupervised fraud-detection toolkit that begins with …

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