Loading...
hidden

View Mobile version

Meta navigation

Startseite – Hochschule Luzern

Language selection and important links

  • Contents
  • Contact
  • Login
  • De
  • En
Search

Main navigation

School navigation

  • Engineering and Architecture
  • Business
  • Computer Science
  • Social Work
  • Art and Design
  • Music

Sub-navigation

  • Degree Programmes
  • Continuing Education
  • Research
  • International
  • Campus
  • About us
  • News

Sub-navigation

Breadcrumbs

  1. Research Research
  2. 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).

Brief information

School:

Business

Status:

Ongoing

Period:

01.01.2022 - 31.12.2024

Overview

The goal of this project is to develop a novel credit risk assessment solution and corresponding methodology for the hospitality sector and small and mid-size enterprises (SMEs) in general which (i) is based on interpretable machine learning methods, (ii) uses novel, alternative data sources, (iii) models spatial correlation, and (iv) allows for making multi-period forecasts.

hidden

Facts

Type of project

Forschung

Internal organisations involved
  • Institute of Financial Services Zug IFZ
Funding
  • Innosuisse - HSLU als Hauptforschungspartnerin (Main Research Partner)
hidden

Persons involved: internal

Project manager
  • Fabio Sigrist
Member of project team
  • Simon Broda
  • Pascal Kündig

Publications

  • Article, review; peer reviewed (2)

    • 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.

Brief information

School:

Business

Status:

Ongoing

Period:

01/01/2022 - 12/31/2024

Project Head

Prof. Dr. Fabio Sigrist

Lecturer

+41 41 757 67 61

Show email

Footer(s)

FH Zentralschweiz

Social media links

  •  Facebook
  •  Instagram
  •  Twitter
  •  LinkedIn
  •  YouTube
  •  Flickr

Contact

Logo Lucerne University of Applied Sciences and Arts

Lucerne University of Applied Sciences and Arts


Werftestrasse 4
6002 Luzern

+41 41 228 42 42

info@hslu.ch

Direct entry

  • A bachelor's degree –
  • A master's degree –
  • Prospective Students (Continuing & Executive Programmes)
  • Unternehmen & Institutionen
  • Media Relations
  • For Students
  • For Members of Staff

Quick link

  • People Finder
  • University Buildings & Campus Locations
  • News
  • Libraries
  • Events
  • Jobs & Karriere
  • Home
  • Hiring Rooms

Static links

  • Newsletter
  • Data protection notice
  • Publishing Acknowledgements
Logo Swissuniversities

QrCode

QrCode
We use cookies on this site to give you the best browsing experience. By continuing to navigate this site or closing this banner you accept this use of cookies. For more information please visit our privacy policy.
OK