CreHos: Novel Machine Learning Algorithms and Alternative Data Sources for Better Credit Risk Assessment for the Hospitality Sector

The goal of this project is to develop a novel credit risk assessment solution and methodology for the hospitality sector and small and mid-size enterprises (SMEs).

Kurzinformation

Departement:

Wirtschaft

Status:

Laufend

Zeitraum:

01.01.2022 - 31.12.2024

Fakten

Projektart

Forschung

Beteiligte interne Organisationen
  • Institut für Finanzdienstleistungen Zug IFZ
Finanzierung
  • Innosuisse - HSLU als Hauptforschungspartnerin (Main Research Partner)

Beteiligte Personen intern

Projektleiter/in
Projektmitarbeiter/in

Publikationen

    • Leuenberger, Nicola & Sigrist, Fabio (2023). Machine learning for corporate default risk: Multi-period prediction, frailty correlation, loan portfolios, and tail probabilities. European Journal of Operational Research, 2023(3), 1390-1406.

    • Sigrist, Fabio (2023). Latent Gaussian Model Boosting. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2023, 1894-1905.