Tim Verdonck
KU Leuven
Fraud detection using analytics
- Venue
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Îlot Bernard du Bois
- Salle 21
AMU - AMSE
5-9 boulevard Maurice Bourdet
13001 Marseille - Date(s)
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Tuesday, June 1 2021
2:00pm to 3:30pm - Contact(s)
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Michel Lubrano: michel.lubrano[at]univ-amu.fr
Pierre Michel: pierre.michel[at]univ-amu.fr
Abstract
Financial institutions increasingly rely on predictive machine learning models to detect fraudulent transactions. Two main challenges when building a supervised tool for fraud detection are the imbalance or skewness of the data and the various costs for different types of misclassification. We discuss techniques to solve the imbalance issue and present a cost-sensitive logistic regression algorithm.