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The Digital Journey of Banking and Insurance, Volume III pp 105–116Cite as

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Kafka—A Practical Implementation of Intraday Liquidity Risk Management

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Abstract

This article shows a practical implementation of intraday liquidity risk management in the high-throughput, low-latency platform of Apache Kafka. The authors describe the business process and challenge in intraday liquidity management. In addition, the chapter delivers a practical application of Apache Kafka in combination with machine learning.

Keywords

  • Intraday liquidity risk management
  • BCBS248
  • Liquidity risk management
  • Clustering analysis
  • Dashboard
  • Apache Kafka
  • Stream processing
  • High throughput
  • Low latency
  • Real-time data feeds

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  • DOI: 10.1007/978-3-030-78821-6_7
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Notes

  1. 1.

    The chapter focuses on streaming technology, but the use case can be easily applied to other design patterns (see Krätz and Morawski 2021).

  2. 2.

    An introduction to Apache Kafka can be found in Steurer (2021) and detailed information is provided in Apache Software Foundation (2020).

  3. 3.

    See Amazon (2020).

  4. 4.

    See Google (2020).

  5. 5.

    For definition see Liermann et al. (2019).

  6. 6.

    System 1 in the concept of cognitive biases by Nobel Memorial Prize in Economic Sciences Laureate Daniel Kahneman, see Kahneman (2011).

Literature

  • Amazon. 2020. Amazon Simple Storage Service S3. Accessed November 28, 2020. https://aws.amazon.com/de/s3/.

  • Apache Software Foundation. 2020. Apache Kafka. Accessed November 28, 2020. https://kafka.apache.org/.

  • Google. 2020. Tensorflow. Accessed November 29, 2020. https://www.tensorflow.org/.

  • Kahneman, Daniel. 2011. Thinking, Fast and Slow. New York: Farrar, Straus and Giroux.

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  • Krätz, Dennis, and Michael Morawski. 2021. “Architecture Patterns—Batch & Real-Time Capabilities.” In The Digital Journey of Banking and Insurance, Volume III—Data Storage, Processing, and Analysis, edited by Volker Liermann and Claus Stegmann. New York: Palgrave Macmillan.

    Google Scholar 

  • Liermann, Volker, Sangmeng Li, and Victoria Dobryashkina. 2019. “Intraday Liquidity—Forecast Using Pattern Recognition.” In The Impact of Digital Transformation and Fintech on the Finance Professional, edited by Volker Liermann and Claus Stegmann. New York: Palgrave Macmillan.

    Google Scholar 

  • Steurer, Ralph. 2021. “Kafka—Real Time Streaming for the Finance Industry.” In The Digital Journey of Banking and Insurance, Volume III—Data Storage, Processing, and Analysis, edited by Volker Liermann and Claus Stegmann. New York: Palgrave Macmillan.

    Google Scholar 

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Author information

Authors and Affiliations

  1. ifb SE, Grünwald, Germany

    Volker Liermann & Sangmeng Li

  2. ifb International AG, Zürich, Switzerland

    Ralph Steurer

Authors
  1. Volker Liermann
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  2. Sangmeng Li
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  3. Ralph Steurer
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Corresponding author

Correspondence to Volker Liermann .

Editor information

Editors and Affiliations

  1. ifb SE, Grünwald, Germany

    Volker Liermann

  2. ifb Americas, Inc., Charlotte, NC, USA

    Claus Stegmann

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Cite this chapter

Liermann, V., Li, S., Steurer, R. (2021). Kafka—A Practical Implementation of Intraday Liquidity Risk Management. In: Liermann, V., Stegmann, C. (eds) The Digital Journey of Banking and Insurance, Volume III. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-78821-6_7

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  • DOI: https://doi.org/10.1007/978-3-030-78821-6_7

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  • Publisher Name: Palgrave Macmillan, Cham

  • Print ISBN: 978-3-030-78820-9

  • Online ISBN: 978-3-030-78821-6

  • eBook Packages: Economics and FinanceEconomics and Finance (R0)