Machine Learning (ML) Technologies for Digital Credit Scoring in Rural Finance: A Literature Review

Rural credit is one of the most critical inputs for farm production across the globe. Despite so many advances in digitalization in emerging and developing economies, still a large part of society like small farm holders, rural youth, and women farmers are untouched by the mainstream of banking tran...

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Autores principales:	Anil Kumar, Suneel Sharma, Mehregan Mahdavi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	machine learning (ML) artificial intelligence (AI) digital credit scoring rural finance credit score micro lending Insurance HG8011-9999
Acceso en línea:	https://doaj.org/article/41a25027305543fe855177734bd377f9
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spelling	oai:doaj.org-article:41a25027305543fe855177734bd377f92021-11-25T18:56:06ZMachine Learning (ML) Technologies for Digital Credit Scoring in Rural Finance: A Literature Review10.3390/risks91101922227-9091https://doaj.org/article/41a25027305543fe855177734bd377f92021-10-01T00:00:00Zhttps://www.mdpi.com/2227-9091/9/11/192https://doaj.org/toc/2227-9091Rural credit is one of the most critical inputs for farm production across the globe. Despite so many advances in digitalization in emerging and developing economies, still a large part of society like small farm holders, rural youth, and women farmers are untouched by the mainstream of banking transactions. Machine learning-based technology is giving a new hope to these individuals. However, it is the banking or non-banking institutions that decide how they will adopt this advanced technology, to have reduced human biases in loan decision making. Therefore, the scope of this study is to highlight the various AI-ML- based methods for credit scoring and their gaps currently in practice by banking or non-banking institutions. For this study, systematic literature review methods have been applied; existing research articles have been empirically reviewed with an attempt to identify and compare the best fit AI-ML-based model adopted by various financial institutions worldwide. The main purpose of this study is to present the various ML algorithms highlighted by earlier researchers that could be fit for a credit assessment of rural borrowers, particularly those who have no or inadequate loan history. However, it would be interesting to recognize further how the financial institutions could be able to blend the traditional and digital methods successfully without any ethical challenges.Anil KumarSuneel SharmaMehregan MahdaviMDPI AGarticlemachine learning (ML)artificial intelligence (AI)digital credit scoringrural financecredit scoremicro lendingInsuranceHG8011-9999ENRisks, Vol 9, Iss 192, p 192 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning (ML) artificial intelligence (AI) digital credit scoring rural finance credit score micro lending Insurance HG8011-9999
spellingShingle	machine learning (ML) artificial intelligence (AI) digital credit scoring rural finance credit score micro lending Insurance HG8011-9999 Anil Kumar Suneel Sharma Mehregan Mahdavi Machine Learning (ML) Technologies for Digital Credit Scoring in Rural Finance: A Literature Review
description	Rural credit is one of the most critical inputs for farm production across the globe. Despite so many advances in digitalization in emerging and developing economies, still a large part of society like small farm holders, rural youth, and women farmers are untouched by the mainstream of banking transactions. Machine learning-based technology is giving a new hope to these individuals. However, it is the banking or non-banking institutions that decide how they will adopt this advanced technology, to have reduced human biases in loan decision making. Therefore, the scope of this study is to highlight the various AI-ML- based methods for credit scoring and their gaps currently in practice by banking or non-banking institutions. For this study, systematic literature review methods have been applied; existing research articles have been empirically reviewed with an attempt to identify and compare the best fit AI-ML-based model adopted by various financial institutions worldwide. The main purpose of this study is to present the various ML algorithms highlighted by earlier researchers that could be fit for a credit assessment of rural borrowers, particularly those who have no or inadequate loan history. However, it would be interesting to recognize further how the financial institutions could be able to blend the traditional and digital methods successfully without any ethical challenges.
format	article
author	Anil Kumar Suneel Sharma Mehregan Mahdavi
author_facet	Anil Kumar Suneel Sharma Mehregan Mahdavi
author_sort	Anil Kumar
title	Machine Learning (ML) Technologies for Digital Credit Scoring in Rural Finance: A Literature Review
title_short	Machine Learning (ML) Technologies for Digital Credit Scoring in Rural Finance: A Literature Review
title_full	Machine Learning (ML) Technologies for Digital Credit Scoring in Rural Finance: A Literature Review
title_fullStr	Machine Learning (ML) Technologies for Digital Credit Scoring in Rural Finance: A Literature Review
title_full_unstemmed	Machine Learning (ML) Technologies for Digital Credit Scoring in Rural Finance: A Literature Review
title_sort	machine learning (ml) technologies for digital credit scoring in rural finance: a literature review
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/41a25027305543fe855177734bd377f9
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Machine Learning (ML) Technologies for Digital Credit Scoring in Rural Finance: A Literature Review

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