Skip to main content

A Statistical Approach Towards Fraud Detection in the Horse Racing

  • Conference paper
  • First Online:
Information Security Applications (WISA 2020)

Abstract

With the inception of online betting in S. Korea, various foreigner professional gambling groups have exploited the betting regulations. This phenomenon has occurred mainly in Asia, because the regulations on gambling in these countries are complex and robust. Our study focuses on the horse racing in S. Korea, which is operated under the government funding. The foreigner gambling groups tried unlimited betting by modifying the official IoT (Internet of Things) based APP arbitrarily. We have checked that some abnormal transactions can occur by modifying this application. Our study proposes a fraud detection method that can help detecting abnormal activities and prevent them. Currently, the Korea Racing Authority (KRA) has been criticized for being ill-equipped to detect abnormal activities with the Walkerhill Incident. Our study presents a new anomaly detection model that uses a flexible window. In this study, we propose an idea that aims to detect abnormal betting transactions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Brooks, G.: Online gambling and money laundering: “views from the inside”. J. Money Launder. Control 15, 304–315 (2012)

    Article  Google Scholar 

  2. Carminati, M., Baggio, A., Maggi, F., Spagnolini, U., Zanero, S.: FraudBuster: temporal analysis and detection of advanced financial frauds. In: Giuffrida, C., Bardin, S., Blanc, G. (eds.) DIMVA 2018. LNCS, vol. 10885, pp. 211–233. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93411-2_10

    Chapter  Google Scholar 

  3. Carpenter, K.: Match-fixing–the biggest threat to sport in the 21st century? Int. Sports Law Rev. 2(1), 13–24 (2012)

    Google Scholar 

  4. Garvey, T.D., Lunt, T.F.: Model based intrusion detection. In: Proceedings of the 14th National Computer Security Conference, vol. 10, pp. 372–385 (1991)

    Google Scholar 

  5. Ghosh, A.K., Schwartzbard, A.: A study in using neural networks for anomaly and misuse detection. In: USENIX Security Symposium, vol. 99, p. 12 (1999)

    Google Scholar 

  6. Halvaiee, N.S., Akbari, M.K.: A novel model for credit card fraud detection using artificial immune systems. Appl. Soft Comput. 24, 40–49 (2014)

    Article  Google Scholar 

  7. Hoffmann, A.O., Birnbrich, C.: The impact of fraud prevention on bank-customer relationships. Int. J. Bank Mark. 30, 390–407 (2012)

    Article  Google Scholar 

  8. Jha, S., Guillen, M., Westland, J.C.: Employing transaction aggregation strategy to detect credit card fraud. Expert Syst. Appl. 39(16), 12650–12657 (2012)

    Article  Google Scholar 

  9. NewsTAPA: Foreign horse racing problem...tens of billions of tax outflows. https://newstapa.org/42021

  10. Richhariya, P., Singh, P.K.: A survey on financial fraud detection methodologies. Int. J. Comput. Appl. 45(22), 15–22 (2012)

    Google Scholar 

  11. Riess, S.A.: The Sport of Kings and the Kings of Crime: Horse Racing, Politics, and Organized Crime in New York 1865–1913. Syracuse University Press, Syracuse (2011)

    Google Scholar 

  12. Veasey, T.J., Dodson, S.J.: Anomaly detection in application performance monitoring data. Int. J. Mach. Learn. Comput. 4(2), 120 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kyungho Lee .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Min, M., Lee, J.J., Park, H., Shin, H., Lee, K. (2020). A Statistical Approach Towards Fraud Detection in the Horse Racing. In: You, I. (eds) Information Security Applications. WISA 2020. Lecture Notes in Computer Science(), vol 12583. Springer, Cham. https://doi.org/10.1007/978-3-030-65299-9_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-65299-9_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-65298-2

  • Online ISBN: 978-3-030-65299-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics