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Rule Learning from Time-Dependent Data Applied to Fraud Detection

Marine Collery 1, 2 
2 IBM France Lab [Biot]
IBM - Paris [Bois-Colombes]
Abstract : In financial environment, fraud detection is a challenging problem with tremendous financial impacts where data is highly unbalanced, sequential and timestamped. An additional constraint comes from the fact that common machine learning methods cannot be used alone for fraud detection, as every decision made in order to label a transaction as fraudulent needs to be explainable and the complete model understandable.The use of a symbolic language, such as understandable classification rules, is therefore preferred or even required.
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Submitted on : Thursday, June 23, 2022 - 10:54:39 AM
Last modification on : Tuesday, June 28, 2022 - 4:43:43 PM


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  • HAL Id : hal-03702564, version 1


Marine Collery. Rule Learning from Time-Dependent Data Applied to Fraud Detection. Rule ML/RR 2021 - Proceedings 5th International Conference on Rule and Reasoning and RuleML, Sep 2021, Leuven, Belgium. ⟨hal-03702564⟩



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