Automating Financial Surveillance

Maria Milosavljevic; Jean-Yves Delort; Ben Hachey; Bavani Arunasalam; Will Radford; James Curran

Mining User-Generated Content for Security

Research Article

Automating Financial Surveillance

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@INPROCEEDINGS{10.1007/978-3-642-12630-7_38,
    author={Maria Milosavljevic and Jean-Yves Delort and Ben Hachey and Bavani Arunasalam and Will Radford and James Curran},
    title={Automating Financial Surveillance},
    proceedings={Mining User-Generated Content for Security},
    proceedings_a={MINUCS},
    year={2012},
    month={10},
    keywords={Financial Surveillance Document Categorisation Machine Learning Sentiment Analysis},
    doi={10.1007/978-3-642-12630-7_38}
}

Maria Milosavljevic
Jean-Yves Delort
Ben Hachey
Bavani Arunasalam
Will Radford
James Curran
Year: 2012
Automating Financial Surveillance
MINUCS
Springer
DOI: 10.1007/978-3-642-12630-7_38

Maria Milosavljevic¹^,*, Jean-Yves Delort^,*, Ben Hachey^,*, Bavani Arunasalam¹^,*, Will Radford^,*, James Curran^,*

1: Capital Markets CRC Limited

*Contact email: maria@cmcrc.com, jydelort@cmcrc.com, bhachey@cmcrc.com, bavani@cmcrc.com, wradford@cmcrc.com, james@cmcrc.com

Abstract

Financial surveillance technology alerts analysts to suspicious trading events. Our aim is to identify explainable false positives (e.g., caused by price-sensitive information in company news) and explainable true positives (e.g., caused by ramping in forums) by aligning these alerts with publicly available information. Our system aligns 99% of alerts, which will speed the analysts’ task by helping them to eliminate false positives and gather evidence for true positives more rapidly.

Keywords: Financial Surveillance Document Categorisation Machine Learning Sentiment Analysis

Published: 2012-10-23

: http://dx.doi.org/10.1007/978-3-642-12630-7_38

Automating Financial Surveillance

Abstract

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