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

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).

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Projektart

Forschung

Beteiligte interne Organisationen

Institut für Finanzdienstleistungen Zug (IFZ)

Finanzierung

Innosuisse - HSLU als Hauptforschungspartnerin (Main Research Partner)

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Beteiligte Personen intern

Projektleiter/in

Fabio Sigrist

Projektmitarbeiter/in

Publikationen

Artikel, Rezension; peer reviewed (3)
- Kündig, Pascal & Sigrist, Fabio (03.12.2024). Iterative Methods for Vecchia-Laplace Approximations for Latent Gaussian Process Models. Journal of the American Statistical Association, 1-14. doi: 10.1080/01621459.2024.2410004
- 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.

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

Kurzinformation

Fakten

Beteiligte Personen intern

Publikationen

Artikel, Rezension; peer reviewed (3)