Financial Inclusion in Emerging Economies: The Application of Machine Learning and Artificial Intelligence in Credit Risk Assessment

TLDR

Credit risk assessment is critical in banking, yet emerging markets lack traditional collateral or identification, limiting access to loans for underbanked individuals. The study employed a literature review of documentary and conceptual sources to evaluate how machine learning and artificial intelligence can improve credit risk assessment. Artificial intelligence and machine learning, using alternative public data, effectively address information asymmetry, adverse selection, and moral hazard, enabling lenders to conduct thorough risk analysis, assess borrower behavior, verify repayment capacity, expand credit access to underserved households, and the study recommends increased investment in these technologies.

Abstract

In banking and finance, credit risk is among the important topics because the process of issuing a loan requires a lot of attention to assessing the possibilities of getting the loaned money back. At the same time in emerging markets, the underbanked individuals cannot access traditional forms of collateral or identification that is required by financial institutions for them to be granted loans. Using the literature review approach through documentary and conceptual analysis to investigate the impact of machine learning and artificial intelligence in credit risk assessment, this study discovered that artificial intelligence and machine learning have a strong impact on credit risk assessments using alternative data sources such as public data to deal with the problems of information asymmetry, adverse selection, and moral hazard. This allows lenders to do serious credit risk analysis, to assess the behaviour of the customer, and subsequently to verify the ability of the clients to repay the loans, permitting less privileged people to access credit. Therefore, this study recommends that financial institutions such as banks and credit lending institutions invest more in artificial intelligence and machine learning to ensure that financially excluded households can obtain credit.