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Applied Data Science in Financial Industry

Natural Language Processing Techniques for Bank Policies

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Research & Innovation Forum 2019 (RIIFORUM 2019)

Part of the book series: Springer Proceedings in Complexity ((SPCOM))

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Abstract

In a time when the employment of Natural Language Processing techniques in domains such as biomedicine, national security, finance and law, is flourishing, this study takes a deep look in its application in policy documents. Besides providing an overview of the current state of the literature that treats these concepts, the study at hand implements a set of unprecedented Natural Language Processing techniques on internal bank policies. The implementation of these techniques, together with the results that derive from the experiment and the experts’ evaluation, introduce a Meta-Algorithmic Modelling framework for processing internal business policies. This framework relies on three Natural Language Processing techniques, namely information extraction, automatic summarization and automatic keyword extraction. For the reference extraction and keyword extraction tasks we calculated Precision, Recall and F-scores. For the former we obtained 0.99, 0.84, and 0.89; for the latter we obtained 0.79, 0.87 and 0.83, respectively. Finally, our summary extraction approach was positively evaluated using a qualitative assessment.

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Authors and Affiliations

  1. Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC, Utrecht, The Netherlands

    Marco Spruit & Drilon Ferati

Authors
  1. Marco Spruit
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  2. Drilon Ferati
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Correspondence to Marco Spruit .

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Editors and Affiliations

  1. Research & Innovation Institute (Rii), Warsaw, Poland

    Anna Visvizi

  2. Deree College, The American College of Greece, Athens, Greece

    Miltiadis D. Lytras

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Spruit, M., Ferati, D. (2019). Applied Data Science in Financial Industry. In: Visvizi, A., Lytras, M. (eds) Research & Innovation Forum 2019. RIIFORUM 2019. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-30809-4_32

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  • DOI: https://doi.org/10.1007/978-3-030-30809-4_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30808-7

  • Online ISBN: 978-3-030-30809-4

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