AI-Based Credit Scoring Models in Microfinance: Improving Loan Accessibility, Risk Assessment, and Financial Inclusion

Abstract

AI has made a huge impact in micro financing through the introduction of credit scoring models that have made loans easily accessible, reduced risks associated with lending while increasing the number and population covered by micro finance. The key traditional credit scoring measures that form the basis of conventional credit scoring models mainly base their decision on historical financial data, thus locking out people who do not have formal records with banks in the society. Symbolic models apply new sources of data, like use of mobile phones, transactions, social media accounts, and biometric data to make new credit scores for borrowers. The application of machine learning models will enhance lending decisions at the microfinance institutions, lower costs of operations, and reduce default risks. Nevertheless, there are certain drawbacks that are associated with the use of big data; issues such as data privacy, algorithms bias, meeting regulatory requirements, and technical constraints for MFIs. This paper aims at reviewing the advancement in credit scoring in the microfinance market, the techniques used to couple credit assessment using Artificial Intelligence, and the concerns towards the ethical practice of deploying Artificial Intelligence. It also provides examples of good practice in AI adoption and explores the potential of AI to increase financial inclusion. Despite the benefits that may be brought about by AI in assisting the process of micro financing, it is crucial to ensure fairness, transparency, and responsible use of the technology in microfinance.