Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/258139 
Erscheinungsjahr: 
2021
Quellenangabe: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 9 [Issue:] 3 [Article No.:] 50 [Publisher:] MDPI [Place:] Basel [Year:] 2021 [Pages:] 1-20
Verlag: 
MDPI, Basel
Zusammenfassung: 
In micro-lending markets, lack of recorded credit history is a significant impediment to assessing individual borrowers' creditworthiness and therefore deciding fair interest rates. This research compares various machine learning algorithms on real micro-lending data to test their efficacy at classifying borrowers into various credit categories. We demonstrate that off-the-shelf multi-class classifiers such as random forest algorithms can perform this task very well, using readily available data about customers (such as age, occupation, and location). This presents inexpensive and reliable means to micro-lending institutions around the developing world with which to assess creditworthiness in the absence of credit history or central credit databases.
Schlagwörter: 
credit risk
credit scoring
default probability
machine learning
micro-credit
micro-finance
micro-lending
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
cc-by Logo
Dokumentart: 
Article
Erscheint in der Sammlung:

Datei(en):
Datei
Größe





Publikationen in EconStor sind urheberrechtlich geschützt.