[ARTICLE] This research demonstrates that machine learning algorithms, particularly random forest classifiers, can effectively classify borrowers into credit categories using readily available data.

by Apostolos Ampountolas (ESSEC Business School), Titus Nyarko Nde, Paresh Date, Corina Constantinescu

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.

[Please read the research paper here]