1 citations · 1 across the 3 of their papers we have counts for
6 papers
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…
Is TabPFN the Silver Bullet for Insurance Pricing?
Bruno Deprez, Wouter Verbeke, Tim Verdonck
Modelling claim frequency and severity for non-life insurance pricing predominantly relies on generalised linear models, with gradient-boosted machines as the leading machine learn…
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…
GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering
Bruno Deprez, Bart Baesens, Tim Verdonck +1
Purpose: We introduce GARG-AML, a fast and transparent graph-based method to catch `smurfing', a common money-laundering tactic. It assigns a single, easy-to-understand risk score…
Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation
Bruno Deprez, Toon Vanderschueren, Bart Baesens +2
Money laundering presents a pervasive challenge, burdening society by financing illegal activities. The use of network information is increasingly being explored to effectively com…
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…