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Dimensionality reduction techniques to support insider trading detection
University College LondonResearchers at Consob and affiliated universities developed an unsupervised machine learning method using dimensionality reduction to support market surveillance in detecting potential insider trading. The approach identifies anomalous investor trading patterns around price-sensitive events by learning features directly from raw data, demonstrating improved robustness and the ability to detect complex trading behaviors missed by prior feature-engineered methods.
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Information Extraction through AI techniques: The KIDs use case at CONSOB
29 Jan 2022
In this paper we report on the initial activities carried out within a
collaboration between Consob and Sapienza University. We focus on Information
Extraction from documents describing financial instruments. We discuss how we
automate this task, via both rule-based and machine learning-based methods and
provide our first results.
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