Do Your Laundry Somewhere Else! Explaining and Improving Money Laundering Detection

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dc.contributor.advisorHeskes, Tom
dc.contributor.advisorWinter Robert de - Manager Cyber Resilience De Volksbank N.V.
dc.contributor.advisorSchnitzeler Olivier - Electronic Channel Security Specialist De Volksbank N.V.
dc.contributor.authorKuijpers, Linde
dc.date.issued2019-07-02
dc.description.abstractArti cial intelligence is applied in more and more high impact elds, such as the banking world. In this elds it is often not acceptable to only give predictions and no explanation. Therefore the eld of Explainable Arti cial Intelligence is also growing. In this thesis a random forest model for money laundering detection for the Volksbank was developed to replace the current rule-based model. This resulted in a large drop in false positives. Two explainable models, SHAP and LIME, were placed over the random forest model and tested on reliability and user friendliness using expert reviews. The SHAP model was preferred over the LIME model.en_US
dc.embargo.lift10000-01-01
dc.embargo.typePermanent embargoen_US
dc.identifier.urihttps://theses.ubn.ru.nl/handle/123456789/10704
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationMaster Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeMasteren_US
dc.titleDo Your Laundry Somewhere Else! Explaining and Improving Money Laundering Detectionen_US
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