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Data Mining in Finance: Current Advances and Future Challenges

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Big Data Analysis: New Algorithms for a New Society

Part of the book series: Studies in Big Data ((SBD,volume 16))

Abstract

Data mining has been successfully applied in many businesses, thus aiding managers to make informed decisions that are based on facts, rather than having to rely on guesswork and incorrect extrapolations. Data mining algorithms equip institutions to predict the movements of financial indicators, enable companies to move towards more energy-efficient buildings, as well as allow businesses to conduct targeted marketing campaigns and forecast sales. Specific data mining success stories include customer loyalty prediction, economic forecasting, and fraud detection. The strength of data mining lies in the fact that it allows for not only predicting trends and behaviors, but also for the discovery of previously unknown patterns. However, a number of challenges remain, especially in this era of big data. These challenges are brought forward due to the sheer Volume of today’s databases, as well as the Velocity (in terms of speed of arrival) and the Variety, in terms of the various types of data collected. This chapter focuses on techniques that address these issues. Specifically, we turn our attention to the financial sector, which has become paramount to business. Our discussion centers on issues such as considering data distributions with high fluctuations, incorporating late arriving data, and handling the unknown. We review the current state-of-the-art, mainly focusing on model-based approaches. We conclude the chapter by providing our perspective as to what the future holds, in terms of building accurate models against today’s business, and specifically financial, data.

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Author information

Authors and Affiliations

  1. National Research Council of Canada, Building M-50, 1200 Montreal Road, Ottawa, Canada

    Eric Paquet & Hongyu Guo

  2. School of Electrical Engineering and Computer Science, University of Ottawa, 800 King Edward Road, Ottawa, Canada

    Eric Paquet & Herna Viktor

Authors
  1. Eric Paquet
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  2. Herna Viktor
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  3. Hongyu Guo
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Corresponding author

Correspondence to Herna Viktor .

Editor information

Editors and Affiliations

  1. University of Ottawa, Ottawa, Ontario, Canada

    Nathalie Japkowicz

  2. Institute of Computing Sciences, Poznań University of Technology, Poznań, Poland

    Jerzy Stefanowski

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Paquet, E., Viktor, H., Guo, H. (2016). Data Mining in Finance: Current Advances and Future Challenges. In: Japkowicz, N., Stefanowski, J. (eds) Big Data Analysis: New Algorithms for a New Society. Studies in Big Data, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-319-26989-4_7

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  • DOI: https://doi.org/10.1007/978-3-319-26989-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26987-0

  • Online ISBN: 978-3-319-26989-4

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