Articlegold2021

A Machine Learning Approach for Micro-Credit Scoring

Apostolos Ampountolas · Titus Nyarko Nde · Paresh M Date · Corina D. Constantinescu

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Financial Distress and Bankruptcy Prediction Microfinance and Financial Inclusion FinTech, Crowdfunding, Digital Finance

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.

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1997A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting 1998Forecasting with artificial neural networks:2017Machine learning models and bankruptcy prediction 2010Consumer credit-risk models via machine-learning algorithms 2004Microfinance: A Comprehensive Review of the Existing Literature 2019The Global Findex Database 2017: Measuring Financial Inclusion and Opportunities to Expand Access to and Use of Financial Services*2017A deep learning ensemble approach for crude oil price forecasting 2018Credit Risk Analysis Using Machine and Deep Learning Models

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