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An Efficient Risk Data Learning with LSTM RNN

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Data Mining (AusDM 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1127))

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Abstract

The use of big risk data for risk management has evolved into a concern within financial services industry in recent years. Whether the quality of big risk data can be relied upon is to be ascertained till 2019. To facilitate the measurement and prediction of data quality, we propose an efficient approach to slide a piece of data from the big risk data and a model to train divergent Long Short-Term Memory (“LSTM”) Recurrent Neural Networks (“RNNs”) with various algorithms. The network is evaluated by the improvement in network run time, prediction accuracy and relevant error. This enables financial institutions to identify potential data risks instantly for earlier mitigation soon.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, University of New South Wales, Sydney, Australia

    Ka Yee Wong & Raymond K. Wong

Authors
  1. Ka Yee Wong
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  2. Raymond K. Wong
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Corresponding author

Correspondence to Raymond K. Wong .

Editor information

Editors and Affiliations

  1. School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, Australia

    Thuc D. Le

  2. La Trobe University, Melbourne, Australia

    Kok-Leong Ong

  3. CSIRO Scientific Computing, Canberra, Australia

    Yanchang Zhao

  4. CSIRO Scientific Computing, Canberra, Australia

    Warren H. Jin

  5. Consilium Technology, Adelaide, Australia

    Sebastien Wong

  6. School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, Australia

    Lin Liu

  7. Microsoft Proprietary Limited, Singapore, Singapore

    Graham Williams

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Wong, K.Y., Wong, R.K. (2019). An Efficient Risk Data Learning with LSTM RNN. In: Le, T., et al. Data Mining. AusDM 2019. Communications in Computer and Information Science, vol 1127. Springer, Singapore. https://doi.org/10.1007/978-981-15-1699-3_10

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  • DOI: https://doi.org/10.1007/978-981-15-1699-3_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1698-6

  • Online ISBN: 978-981-15-1699-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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