1 citations · 1 across the 4 of their papers we have counts for
5 papers · 1 filter
Foundation Models for Credit Risk Prediction: A Game Changer?
Bart Baesens, Andreas Goethals, Stefan Lessmann +10
Predictive models play a pivotal role in credit risk management, guiding critical decisions through accurate estimation of default probabilities and losses. Extensive research has…
Inductive inference of gradient-boosted decision trees on graphs for insurance fraud detection
Félix Vandervorst, Félix Vandervorst, Bruno Deprez +2
Graph-based methods are becoming increasingly popular in machine learning due to their ability to model complex data and relations. Insurance fraud is a prime use case, since fraud…
Uplift modeling with continuous treatments: A predict-then-optimize approach
Simon De Vos, Christopher Bockel-Rickermann, Stefan Lessmann +1
The goal of uplift modeling is to recommend actions that optimize specific outcomes by determining which entities should receive treatment. One common approach involves two steps:…
Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive Review
Bruno Deprez, Wei Wei, Wouter Verbeke +3
Financial institutions are required by regulation to report suspicious financial transactions related to money laundering. Therefore, they need to constantly monitor vast amounts o…
Sources of Gain: Decomposing Performance in Conditional Average Dose Response Estimation
Christopher Bockel-Rickermann, Toon Vanderschueren, Tim Verdonck +1
Estimating conditional average dose responses (CADR) is an important but challenging problem. Estimators must correctly model the potentially complex relationships between covariat…