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Development of novel fraud detection and credit risk assessment methods for SME credits. The goal is to develop machine learning based algorithms and models that combine various types of data in order to obtain improved predictive accuracy.
Wirtschaft
Abgeschlossen
01.01.2017 - 01.03.2019
Forschung
Sigrist, Fabio (2022). Gaussian Process Boosting. Journal of Machine Learning Research (JMLR), 2022(23), 1-46.
Sigrist, Fabio (2021). KTBoost: Combined kernel and tree boosting. Neural Processing Letters, 2021(2), 1147-1160.
Sigrist, Fabio (2021). Gradient and newton boosting for classification and regression. Expert Systems With Applications, 2021(167), 114080.
Schliessen
Hirnschall, Christoph & Sigrist, Fabio (2019). Grabit: Gradient tree-boosted Tobit models for default prediction. Journal of Banking & Finance, 2019(102), 177-192.
