Corpus ID: 53786597

Computer-Assisted Fraud Detection, From Active Learning to Reward Maximization

  title={Computer-Assisted Fraud Detection, From Active Learning to Reward Maximization},
  author={Christelle Marfaing and A. Garcia},
  • Christelle Marfaing, A. Garcia
  • Published 2018
  • Computer Science, Mathematics
  • ArXiv
  • The automatic detection of frauds in banking transactions has been recently studied as a way to help the analysts finding fraudulent operations. Due to the availability of a human feedback, this task has been studied in the framework of active learning: the fraud predictor is allowed to sequentially call on an oracle. This human intervention is used to label new examples and improve the classification accuracy of the latter. Such a setting is not adapted in the case of fraud detection with… CONTINUE READING
    1 Citations


    Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization
    • 20
    • PDF
    Statistical fraud detection: A review
    • 1,007
    • PDF
    Data mining for credit card fraud: A comparative study
    • 479
    • PDF
    Learning on the border: active learning in imbalanced data classification
    • 304
    • PDF
    Dynamic Credit-Card Fraud Profiling
    • 2
    • PDF
    Infinitely Imbalanced Logistic Regression
    • A. Owen
    • Computer Science, Mathematics
    • J. Mach. Learn. Res.
    • 2007
    • 74
    • PDF
    Online Adaptive Asymmetric Active Learning for Budgeted Imbalanced Data
    • 18
    • PDF
    Online Asymmetric Active Learning with Imbalanced Data
    • 19
    Active Learning by Learning
    • 56
    • PDF
    Learning Active Learning from Data
    • 98
    • Highly Influential
    • PDF