GOLDRUSH: Rule sharing system for fraud detection

Ariel Jarovsky, Tova Milo, Slava Novgorodov, Wang Chiew Tan

Research output: Contribution to journalConference articlepeer-review

7 Scopus citations


Fraud detection rules, written by domain experts, are often employed by financial companies to enhance their machine learningbased mechanisms for accurate detection of fraudulent transactions. Accurate rule writing is a challenging task where domain experts spend significant effort and time. A key observation is that much of this difficulty originates from the fact that experts typically work as “lone rangers“ or in isolated groups to define the rules, or work on detecting frauds in one context in isolation from frauds that occur in another context. However, in practice there is a lot of commonality in what different experts are trying to achieve. In this demo, we present the GOLDRUSH system, which facilitates knowledge sharing via effective adaptation of fraud detection rules from one context to another. GOLDRUSH abstracts the possible semantic interpretations of each of the conditions in the rules in one context and adapts them to the target context. Efficient algorithms are used to identify the most effective rule adaptations w.r.t a given cost-benefit metric. We showcase GOLDRUSH through a reenactment of a real-life fraud detection event. Our demonstration will engage the VLDB'18 audience, allowing them to play the role of experts collaborating in the fight against financial frauds.

Original languageEnglish
Pages (from-to)1998-2001
Number of pages4
JournalProceedings of the VLDB Endowment
Issue number12
StatePublished - 2018
Event44th International Conference on Very Large Data Bases, VLDB 2018 - Rio de Janeiro, Brazil
Duration: 27 Aug 201831 Aug 2018


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