17 citations · 25 across the 5 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…
On the Performance of LLMs for Real Estate Appraisal
Margot Geerts, Manon Reusens, Bart Baesens +2
The real estate market is vital to global economies but suffers from significant information asymmetry. This study examines how Large Language Models (LLMs) can democratize access…
Discovering High-Quality Process Models Despite Data Scarcity
Jan Niklas Adams, Jari Peeperkorn, Tobias Brockhoff +6
Process discovery algorithms learn process models from executed activity sequences, describing concurrency, causality, and conflict. Concurrent activities require observing multipl…
Can recurrent neural networks learn process model structure?
Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt
Various methods using machine and deep learning have been proposed to tackle different tasks in predictive process monitoring, forecasting for an ongoing case e.g. the most likely…
Can deep neural networks learn process model structure? An assessment framework and analysis
Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt
Predictive process monitoring concerns itself with the prediction of ongoing cases in (business) processes. Prediction tasks typically focus on remaining time, outcome, next event…
Profit Driven Decision Trees for Churn Prediction
Sebastiaan Höppner, Eugen Stripling, Bart Baesens +2
Customer retention campaigns increasingly rely on predictive models to detect potential churners in a vast customer base. From the perspective of machine learning, the task of pred…