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

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Issue Date
2019-07-02
Language
en
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Abstract
Arti 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.
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Faculteit der Sociale Wetenschappen