Machine learning based fraud detection and credit risk assessment for SME credits

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.

Kurzinformation

Departement:

Wirtschaft

Status:

Abgeschlossen

Zeitraum:

01.01.2017 - 01.03.2019

Fakten

Projektart

Forschung

Beteiligte interne Organisationen
  • Institut für Finanzdienstleistungen Zug IFZ
  • CC Financial Services
  • CC Corporate Finance
Finanzierung
  • KTI-HSLU als Hauptgesuchsteller/in
  • Private / Stiftungen

Beteiligte Personen intern

Projektleiter/in
Projektmitarbeiter/in

Publikationen

    • 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.

    • Hirnschall, Christoph & Sigrist, Fabio (2019). Grabit: Gradient tree-boosted Tobit models for default prediction. Journal of Banking & Finance, 2019(102), 177-192.