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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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    1. José Francisco Martínez Sánchez & Gilberto Pérez Lechuga, 2016. "Assessment of a credit scoring system for popular bank savings and credit," Contaduría y Administración, Accounting and Management, vol. 61(2), pages 391-417, Abril-Jun.
    2. Onder Ozgur & Erdal Tanas Karagol & Fatih Cemil Ozbugday, 2021. "Machine learning approach to drivers of bank lending: evidence from an emerging economy," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-29, December.
    3. Saba Moradi & Farimah Mokhatab Rafiei, 2019. "A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-27, December.
    4. Zeng, Yiwu & Jia, Fu & Wan, Li & Guo, Hongdong, 2017. "E-commerce in agri-food sector: a systematic literature review," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 20(4), February.
    5. Lkhagvadorj Munkhdalai & Tsendsuren Munkhdalai & Oyun-Erdene Namsrai & Jong Yun Lee & Keun Ho Ryu, 2019. "An Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments," Sustainability, MDPI, vol. 11(3), pages 1-23, January.
    6. Martin Leo & Suneel Sharma & K. Maddulety, 2019. "Machine Learning in Banking Risk Management: A Literature Review," Risks, MDPI, vol. 7(1), pages 1-22, March.
    7. José Américo Pereira Antunes, 2021. "To supervise or to self-supervise: a machine learning based comparison on credit supervision," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-21, December.
    8. Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.
    9. Ta Nhat Linh & Hoang Thanh Long & Le Van Chi & Le Thanh Tam & Philippe Lebailly, 2019. "Access to Rural Credit Markets in Developing Countries, the Case of Vietnam: A Literature Review," Sustainability, MDPI, vol. 11(5), pages 1-18, March.
    10. Maisa Cardoso Aniceto & Flavio Barboza & Herbert Kimura, 2020. "Machine learning predictivity applied to consumer creditworthiness," Future Business Journal, Springer, vol. 6(1), pages 1-14, December.
    11. Shorouq Fathi Eletter & Saad Ghaleb Yaseen & Ghaleb Awad Elrefae, 2010. "Neuro-Based Artificial Intelligence Model for Loan Decisions," American Journal of Economics and Business Administration, Science Publications, vol. 2(1), pages 27-34, March.
    12. Lean Yu & Xinxie Li & Ling Tang & Zongyi Zhang & Gang Kou, 2015. "Social credit: a comprehensive literature review," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 1(1), pages 1-18, December.
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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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