Clustering Framework Detects Suspicious Trading Patterns in Capital Markets
Summary
This study introduces an unsupervised fraud-detection toolkit utilizing K-Means++ clustering to identify suspicious trading patterns in capital markets. Analyzing a large dataset of financial transactions, the framework flagged 2.02% of trades as suspicious, categorizing them into types like spoofing, pump and dump, and insider trading.
Why it matters
For professionals in financial services and regulatory bodies, this framework offers a valuable tool to proactively detect and categorize various forms of market manipulation, enhancing market integrity and protecting investors.
How to implement this in your domain
- 1Integrate unsupervised clustering algorithms, like K-Means++, into existing fraud detection systems.
- 2Collect and preprocess large datasets of financial transaction data for pattern analysis.
- 3Define and apply market practice heuristic thresholds to categorize identified suspicious patterns.
- 4Develop a system for continuous monitoring of trading activities using the clustering framework.
- 5Collaborate with compliance and regulatory teams to investigate and act upon flagged suspicious trades.
Who benefits
Key takeaways
- Unsupervised clustering can effectively identify suspicious trading patterns without prior labels.
- A significant percentage of trades can be flagged as potentially manipulative.
- Heuristic thresholds help categorize suspicious activities like spoofing and insider trading.
- Such tools enhance market integrity and aid in fraud detection.
Original post by Asif Zaman, Romona Magdalene Sarkar, Sabiha Khair Ohi, Iftekharul Mobin
"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…"
View on XOriginally posted by Asif Zaman, Romona Magdalene Sarkar, Sabiha Khair Ohi, Iftekharul Mobin on X · view source
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