Year: 2021
Type: article
Abstract: 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.
Source: Risks
DOI: 10.3390/risks9030050
Language: English
Subfield: Accounting
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,512
OpenAlex ID: W3135429893