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

David Mhlanga

doi:10.3390/ijfs9030039

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.

Highlights

Using the literature review approach through content analysis and conceptual analysis of authoritative documents such as governments reports, international statistics, media articles, peer-reviewed journal articles, books to investigate the impact of machine learning and artificial intelligence in credit risk assessment, the study discovered that AI and machine learning has a strong impact on credit risk assessments
It was discovered that using alternative data, the problems that affect the credit market which mainly manifest through information asymmetries such as moral hazard and adverse selection can be dealt with if AI and machine learning are applied to alternative data
Social networks which are powered by AI can signal information in a much more accurate fashion than what human agents can do which can help to solve the problem of information asymmetry in the credit market

Summary

Introduction

Credit risk is among the important topics because the process of issuing a loan requires a lot of attention to assess the possibilities of getting the money back (Danėnas and Garšva 2010). There are many different statistical, mathematical as well as intelligent models used in the process of predicting and analysing risk (Gu et al 2018). These techniques are used concerning the circumstances around the scoring and evaluation of probability default. Thorat (2007) came up with a definition of financial inclusion where financial inclusion is defined as how financial services are provided at an affordable rate by the formal financial institutions to the disadvantaged groups. Some scholars came forth with the definitions of financial exclusion and in some instances, these terms are financial inclusion and financial exclusion (Mhlanga 2020)

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Conclusion