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The Digital Journey of Banking and Insurance, Volume II pp 33–49Cite as

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Use Case—Fraud Detection Using Machine Learning Techniques

Abstract

The article describes the challenges and obstacles in fraud prevention in insurance claims management. The common algorithms like autoencoder and more complex anomaly detection algorithms are discussed.

Keywords

  • Fraud Detection
  • Machine Learning
  • Pattern Recognition
  • Process Optimization

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  • DOI: 10.1007/978-3-030-78829-2_3
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Literature

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  • Kaggle. 2018. Auto Insurance Claims Data, August 20. Accessed September 27, 2020. https://www.kaggle.com/buntyshah/auto-insurance-claims-data.

  • Liermann, Volker, Sangmeng Li, and Norbert Schaudinnus. 2019. “Batch Processing—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.

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  • Liermann, Volker, Sangmeng Li, and Norbert Schaudinnus. 2019. “Mathematical Background of Machine Learning.” In The Impact of Digital Transformation and Fintech on the Finance Professional, edited by Volker Liermann and Claus Stegmann. New York: Palgrave Macmillan.

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  • Velauthapillai, Jeyakrishna and Johannes Floß. 2021. “Special Data for Insurance Companies.” 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.

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Author information

Authors and Affiliations

  1. ifb SE, Grünwald, Germany

    Philipp Enzinger & Sangmeng Li

Authors
  1. Philipp Enzinger
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  2. Sangmeng Li
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Corresponding author

Correspondence to Philipp Enzinger .

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

Enzinger, P., Li, S. (2021). Use Case—Fraud Detection Using Machine Learning Techniques. In: Liermann, V., Stegmann, C. (eds) The Digital Journey of Banking and Insurance, Volume II. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-78829-2_3

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  • DOI: https://doi.org/10.1007/978-3-030-78829-2_3

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  • Publisher Name: Palgrave Macmillan, Cham

  • Print ISBN: 978-3-030-78828-5

  • Online ISBN: 978-3-030-78829-2

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