Rudolf: Interactive Rule Refinement System for Fraud Detection. Milo, T., Novgorodov, S., & Tan, W. Proceedings of the Vldb Endowment, 9(13):1465-1468, 2016.
Rudolf: Interactive Rule Refinement System for Fraud Detection [pdf]Paper  abstract   bibtex   
Credit card frauds are unauthorized transactions that are made or attempted by a person or an organization that is not authorized by the card holders. In addition to machine learning-based techniques, credit card companies often employ domain experts to manually specify rules that exploit domain knowledge for improving the detection process. Over time, however, as new (fraudulent and legitimate) transaction arrive, these rules need to be updated and refined to capture the evolving (fraud and legitimate) activity patterns. The goal of the RUDOLF system that is demonstrated here is to guide and assist domain experts in this challenging task. RUDOLF automatically determines a best set of candidate adaptations to existing rules to capture all fraudulent transactions and, respectively, omit all legitimate transactions. The proposed modifications can then be further refined by domain experts based on their domain knowledge, and the process can be repeated until the experts are satisfied with the resulting rules. Our experimental results on real-life datasets demonstrate the effectiveness and efficiency of our approach. We showcase RUDOLF with two demonstration scenarios: detecting credit card frauds and network attacks. Our demonstration will engage the VLDB audience by allowing them to play the role of a security expert, a credit card fraudster, or a network attacker.
@article{
 title = {Rudolf: Interactive Rule Refinement System for Fraud Detection},
 type = {article},
 year = {2016},
 identifiers = {[object Object]},
 pages = {1465-1468},
 volume = {9},
 id = {cbb76e09-2e62-3206-a3ad-7e69612dcd2a},
 created = {2018-01-04T18:35:21.675Z},
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 last_modified = {2018-01-10T17:12:33.401Z},
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 citation_key = {Milo2016a},
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 abstract = {Credit card frauds are unauthorized transactions that are made or
attempted by a person or an organization that is not authorized by the
card holders. In addition to machine learning-based techniques, credit
card companies often employ domain experts to manually specify rules
that exploit domain knowledge for improving the detection process. Over
time, however, as new (fraudulent and legitimate) transaction arrive,
these rules need to be updated and refined to capture the evolving
(fraud and legitimate) activity patterns. The goal of the RUDOLF system
that is demonstrated here is to guide and assist domain experts in this
challenging task.
RUDOLF automatically determines a best set of candidate adaptations to
existing rules to capture all fraudulent transactions and, respectively,
omit all legitimate transactions. The proposed modifications can then be
further refined by domain experts based on their domain knowledge, and
the process can be repeated until the experts are satisfied with the
resulting rules. Our experimental results on real-life datasets
demonstrate the effectiveness and efficiency of our approach. We
showcase RUDOLF with two demonstration scenarios: detecting credit card
frauds and network attacks. Our demonstration will engage the VLDB
audience by allowing them to play the role of a security expert, a
credit card fraudster, or a network attacker.},
 bibtype = {article},
 author = {Milo, Tova and Novgorodov, Slava and Tan, Wang-Chiew},
 journal = {Proceedings of the Vldb Endowment},
 number = {13}
}
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