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

Author

Listed:
  • Anil Kumar

    (Doctor of Business Administration (DBA), SP Jain School of Global Management, Mumbai 400070, Maharashtra, India)

  • Suneel Sharma

    (SkyzHotech Solutions Pvt. Ltd., Bengaluru 560102, India)

  • Mehregan Mahdavi

    (Sydney International School of Technology and Commerce (SISTC), Sydney, NSW 2000, Australia)

Abstract

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.

Suggested Citation

  • Anil Kumar & Suneel Sharma & Mehregan Mahdavi, 2021. "Machine Learning (ML) Technologies for Digital Credit Scoring in Rural Finance: A Literature Review," Risks, MDPI, vol. 9(11), pages 1-15, October.
    • Handle: RePEc:gam:jrisks:v:9:y:2021:i:11:p:192-:d:669198
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    References listed on IDEAS

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    Cited by:

    1. Yangyang Zheng & Jianhong Lou & Linfeng Mei & Yushuang Lin, 2023. "Research on Digital Credit Behavior of Farmers’ Cooperatives—A Grounded Theory Analysis Based on the “6C” Family Model," Agriculture, MDPI, vol. 13(8), pages 1-19, August.
    2. Jiali Zhou & Xiangbo Fan & Chenggang Li & Guofei Shang, 2022. "Factors Influencing the Coupling of the Development of Rural Urbanization and Rural Finance: Evidence from Rural China," Land, MDPI, vol. 11(6), pages 1-21, June.

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