Skip to main content
SpringerLink
Log in
Book cover

International Conference on Information Systems, Technology and Management

ICISTM 2009: Information Systems, Technology and Management pp 356–357Cite as

An Overview of a Hybrid Fraud Scoring and Spike Detection Technique for Fraud Detection in Streaming Data

  • Conference paper

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 31))

Abstract

Credit card and personal loan applications have increased significantly. Application fraud is present when the application forms contain plausible and synthetic identity information or real stolen identity information. The monetary cost of application fraud is often estimated to be in the billions of dollars [1].

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

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 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

Learn about institutional subscriptions

References

  1. Kou, Y., Lu, C., Sinvongwattana, S., Huang, Y.P.: Survey of Fraud Detection Techniques. In: Proc. of IEEE Networking, Taiwan, March 21-23 (2004)

    Google Scholar 

  2. Bolton, R.J., Hand, D.J.: Statistical Fraud Detection: A Review (2002)

    Google Scholar 

  3. Phua, C., Gayler, R., Lee, V., Smith-Miles, K.: Adaptive Spike Detection for Resilient Data Stream Mining. In: Proc. of 6th Australasian Data Mining Conf. (2007)

    Google Scholar 

  4. Fan, W., Huang, Y.-a., Wang, H., Yu, P.S.: Active Mining of Data Streams. IBM T. J. Watson Research, Hawthorne, NY 10532 (2006)

    Google Scholar 

  5. Phua, C., Lee, V., Smith-Miles, K.: The Personal Name Problem and a Recommended Data Mining Solution. In: Encyclopedia of Data Warehousing and Mining, Australian (2007)

    Google Scholar 

  6. Christen, P.: Probabilistic Data Generation for Deduplication and Data Linkage. Australian National University (Visited: 20-10-2008), http://datamining.anu.edu.au/linkage.html

  7. Witten, I., Frank, E.: Data Mining: Practical Attribute Ranking Machine Learning Tools and Techniques. Morgan Kaufmann, Elsevier, San Francisco (2005)

    MATH  Google Scholar 

  8. Phua, C., Gayler, R., Lee, V., Smith-Miles, K.: On the Communal Analysis Suspicion Scoring for Identity Crime in Streaming Credit Applications. European J. of Operational Research (2006)

    Google Scholar 

  9. Macskassy, S., Provost, F.: Suspicion Scoring based on Guilt-by-Association, Collective Inference. In: Proc. of Conf. on Intelligence Analysis (2005)

    Google Scholar 

  10. Phua, C., Gayler, R., Lee, V., Smith, K.: Temporal representation in spike detection of sparse personal identity streams. In: Chen, H., Wang, F.-Y., Yang, C.C., Zeng, D., Chau, M., Chang, K. (eds.) WISI 2006. LNCS, vol. 3917, pp. 115–126. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  11. Li, T., Li, Q., Zhu, S., Ogihara, M.: A Survey on Wavelet Applications in Data Mining 4(2), 49–69 (2002)

    Google Scholar 

  12. Steiner, S.H.: Exponentially Weighted Moving Average Control Charts with Time Varying Control Limits and Fast Initial Respons. Journal of Quality Technology 31, 75–86 (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Iran University of Science and Technology, Tehran, Iran

    Naeimeh Laleh & Mohammad Abdollahi Azgomi

Authors
  1. Naeimeh Laleh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Abdollahi Azgomi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. of Computer Science, Georgia State University, 34 Peachtree Street, P.O. Box, Atlanta, GA, USA

    Sushil K. Prasad

  2. Institute of Management Technology, Ghaziabad, India

    Susmi Routray  & Reema Khurana  & 

  3. Department of Computer and Information Science and Technology, University of Florida, USA

    Sartaj Sahni

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Laleh, N., Azgomi, M.A. (2009). An Overview of a Hybrid Fraud Scoring and Spike Detection Technique for Fraud Detection in Streaming Data. In: Prasad, S.K., Routray, S., Khurana, R., Sahni, S. (eds) Information Systems, Technology and Management. ICISTM 2009. Communications in Computer and Information Science, vol 31. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00405-6_45

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-00405-6_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00404-9

  • Online ISBN: 978-3-642-00405-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation