Skip to main content

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

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-78829-2_3
  • Chapter length: 17 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   69.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-78829-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD   89.99
Price excludes VAT (USA)
Fig. 1

(© ifb SE)

Fig. 2

(© ifb SE)

Fig. 3

(© ifb SE)

Fig. 4

(© ifb SE)

Fig. 5

(© ifb SE)

Fig. 6

(© ifb SE)

Fig. 7

(© ifb SE)

Fig. 8

(© ifb SE)

Fig. 9

(© ifb SE)

Fig. 10

(© ifb SE)

Literature

  • Hartung, Sören and Manuela Führer. 2021. “AI for Impairment Accounting.” In The Digital Journey of Banking and Insurance, Volume I—Disruption and DNA, edited by Volker Liermann and Claus Stegmann. New York: Palgrave Macmillan.

    Google Scholar 

  • H2O.ai. 2019. h2o.ai Overview, January 29. Accessed September 29, 2020. http://docs.h2o.ai/h2o/latest-stable/h2o-docs/index.html.

  • insurance europe. 2019. Insurance Fraud: Not a Victimless Crime. Brussels: Insurance Europe aisbl, November.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • McKinsey & Company. 2015. Claims Management: Taking a Determined Stand Against Insurance Fraud. Germany: Munich.

    Google Scholar 

  • 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.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Philipp Enzinger .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Verify currency and authenticity via CrossMark

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

Download citation

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

  • Published:

  • Publisher Name: Palgrave Macmillan, Cham

  • Print ISBN: 978-3-030-78828-5

  • Online ISBN: 978-3-030-78829-2

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