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.