L1 -penalized fraud detection support vector machines

Minhyoung Park, Hyungwoo Kim, Seung Jun Shin

Research output: Contribution to journalArticlepeer-review


Standard binary classifiers that maximize the overall accuracy fail in fraud detection where very few fraud cases are concealed within a large number of normal ones, as the best accuracy is often achieved by ignoring all fraud cases. In such a scenario, a natural alternative is what we refer to as a fraud-detection support vector machine, which never fails to detect fraud during training. In this article, we propose the L1-penalized fraud-detection SVM that is capable of efficiently detecting fraud cases and selecting informative variables simultaneously. We establish the piecewise linearity of the L1-penalized fraud detection SVM as a function of the regularization parameter and then develop an efficient algorithm for computing its entire regularization paths, greatly facilitating its tuning. The advantages of the L1-penalized fraud detection SVM and its path algorithm for fraud detection are numerically demonstrated using both simulated and real data sets.

Original languageEnglish
Pages (from-to)420-439
Number of pages20
JournalJournal of the Korean Statistical Society
Issue number2
Publication statusPublished - 2023 Jun

Bibliographical note

Funding Information:
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MIST), Grant number 2018R1D1A1B07043034 and 2022M3J6A1063595.

Publisher Copyright:
© 2023, Korean Statistical Society.


  • Entire regularization paths
  • Fraud detection
  • L-penalized SVM
  • Variable selection

ASJC Scopus subject areas

  • Statistics and Probability


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