Investigating the Use of Machine Learning in Automating Credit Scoring for Microfinance

Abstract

Machine learning (ML) has emerged as a transformative tool in automating credit scoring, particularly in the domain of microfinance, where access to credit often hinges on unconventional data and rapid decision-making. This research investigates the integration of ML algorithms into credit scoring models for microfinance, focusing on their ability to assess creditworthiness using diverse data sources such as transactional histories, social behavior, and mobile usage patterns. By automating the traditionally manual and subjective processes of credit evaluation, ML offers increased accuracy, scalability, and efficiency, enabling financial institutions to serve underserved populations more effectively. The study explores various ML techniques, including decision trees, random forests, and neural networks, and evaluates their performance in predicting default risk. Challenges such as data privacy, algorithmic bias, and the interpretability of ML models are also addressed. Through case studies and experimental validation, this research aims to provide actionable insights into leveraging ML for credit scoring in microfinance, highlighting its potential to improve financial inclusion and operational efficiency.